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Keywords:

  • canopy cover;
  • data screening;
  • fix rate;
  • GPS;
  • location error;
  • Lotek;
  • PDOP;
  • telemetry;
  • terrain obstruction

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Study area
  5. Methods
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References
  • 1
    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.
  • 2
    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.
  • 3
    The fix rate for test collars was very high in all habitats (mean 99·5%, SE 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 (CEP) 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 m 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.
  • 4
    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%.
  • 5
    Synthesis and applications. 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.

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Study area
  5. Methods
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References

Global positioning system (GPS) telemetry allows for the continuous acquisition of location data at frequent time intervals for individual animals over long periods of time. Such detailed information can be used to evaluate wildlife movements, space use and resource selection with a high degree of precision and accuracy (Rodgers 2001). However, two types of errors can bias analyses based on GPS locations: missed location fixes and location error (D’Eon et al. 2002; Frair et al. 2004). Several studies have reported the effects of different habitat variables on GPS fix acquisition (Moen et al. 1996; Dussault et al. 1999; D’Eon et al. 2002; Frair et al. 2004) and location errors (Rempel, Rodgers & Abraham 1995; Moen et al. 1996; Di Orio, Callas & Schaefer 2003; D’Eon & Delparte 2005). However, no study has examined how continuous ranges of canopy cover and terrain simultaneously affect location error at different levels of positional dilution of precision (PDOP) and signal quality (D’Eon & Delparte 2005). This information can assist in developing a protocol for removing large location errors from GPS data sets.

The first type of error inherent to GPS technology, unsuccessful fix acquisition, leads to missing location data (D’Eon et al. 2002; Frair et al. 2004). Fix rates for stationary GPS collars can range from 68% to 100%, with most collars achieving rates > 85%; however, fix rates can be as low as 13% (Frair et al. 2004). Environmental factors that can affect reception of GPS satellite signals and result in failed location acquisition include a high percentage of tree canopy cover (Rempel & Rodgers 1997; Obbard, Pond & Perera 1998; D’Eon et al. 2002; Di Orio, Callas & Schaefer 2003; Frair et al. 2004), cover types (Di Orio, Callas & Schaefer 2003), the interaction between canopy cover and terrain (D’Eon et al. 2002; Frair et al. 2004), time of day (Moen, Pastor & Cohen 1997; Frair et al. 2004; but see D’Eon et al. 2002) and time of year (Dussault et al. 2001; Frair et al. 2004). Also, in the northern hemisphere, GPS collars can have higher fix rates on south-facing slopes compared with north-facing slopes (D’Eon & Delparte 2005). Therefore GPS collar location data may be biased towards acquiring satellite fixes in more open habitats with favourable topography. In addition, animal behaviour may dramatically affect GPS collar fix rates (Dussault et al. 2001; D’Eon 2003; D’Eon & Delparte 2005). Collar location acquisition schedules can also influence fix rates, with higher fix rates observed for collars that attempt location fixes at shorter time intervals (Cain et al. 2005). To counter biases associated with missed locations, correction factors may be developed and applied to GPS location data sets (Frair et al. 2004).

The second error type, location error, is inherent in all telemetry systems and can lead to misclassification of habitats used in studies of resource selection (Rodgers 2001) and bias estimates of movement paths (DeCesare, Squires & Kolbe 2005). Location error is largely influenced by habitat components, such as canopy cover (D’Eon et al. 2002; Di Orio, Callas & Schaefer 2003) and terrain obstruction (D’Eon et al. 2002; Cain et al. 2005), although atmospheric conditions can also contribute. As selective availability (a USA government strategy imposed on the collection of location data to make locations unpredictably inaccurate for national defence concerns) was discontinued in May 2000, most stationary test locations are reported to be accurate to within 30 m (D’Eon et al. 2002) and can be accurate to < 3 m (Rodgers 2001). Two-dimensional (2-D) and three-dimensional (3-D) fixes are recorded when the GPS unit simultaneously contacts three and ≥ 4 satellites, respectively, with 3-D fixes generally more accurate than 2-D fixes (Moen et al. 1996; Rempel & Rodgers 1997; Di Orio, Callas & Schaefer 2003). Increasing canopy cover may result in fewer 3-D fixes and more 2-D fixes as a result of satellite signal obstruction (Rempel, Rodgers & Abraham 1995).

A PDOP value, which measures satellite geometry, is recorded for each GPS location. Lower PDOP readings indicate wider satellite spacing, which potentially minimizes triangulation error and results in more accurate location estimates (D’Eon et al. 2002; D’Eon & Delparte 2005). Because PDOP is related to location error (D’Eon et al. 2002), screening out location data with high PDOP values has been suggested to reduce location error (Moen et al. 1996; Dussault et al. 2001; D’Eon & Delparte 2005). Also, the screening of 2-D fixes from data sets may substantially decrease location error (Moen et al. 1996; D’Eon et al. 2002). While screening data sets can reduce location errors, it also may lead to significant reductions of location data and introduce additional biases into analyses of animal locations. More detailed investigation of data screening is necessary to evaluate the trade-offs between eliminating inaccurate locations and retaining the maximum amount of location data. Our objectives were to quantify how habitat affected GPS collar performance across the range of variation within a mountainous region of northern Idaho, USA, and evaluate different data screening options. Specifically, we assessed: (i) how canopy cover and terrain obstruction simultaneously affected fix rate, proportion of 3-D fixes, location error and PDOP values; (ii) the relationship between PDOP values and location error; and (iii) how four data screening options affected location error and data reduction for GPS location data sets from stationary test collars and free-ranging black bears Ursus americanus.

Study area

  1. Top of page
  2. Summary
  3. Introduction
  4. Study area
  5. Methods
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References

The study area was located in the Purcell Mountains of northern Idaho, USA, and was bordered to the west by the Kootenai River, north by Canada, east by Montana, and south by Highway 2. The area was approximately 300 km2 in size. Highway 95 ran through the centre of the study area and an extensive network of secondary roads was present because of timber harvest. The topography was varied, ranging from flat valley bottoms to steep and rugged mountain slopes, with altitudes ranging from 700 m to > 1900 m. The regional climate was Pacific maritime, with cold, snowy winters and short, warm summers, with annual precipitation averaging 100–150 cm, mostly in the form of snow (Kasworm, Carriles & Radandt 2004). Forest predominated throughout the study area, although extensive open areas were also present in meadow sites and recently logged timber stands. At drier, lower altitudes, common tree species included ponderosa pine Pinus ponderosa, western larch Larix occidentalis and Douglas fir Pseudotsuga menziesii. More mesic, low-altitude stands consisted of grand fir Abies grandis, western red cedar Thuja plicata, western hemlock Tsuga heterophylla and black cottonwood Populus trichocarpa. Subalpine fir Abies lasiocarpa, Engelmann spruce Picea engelmannii and mountain hemlock Tsuga mertensiana occurred at higher altitudes above 1500 m, and lodgepole pine Pinus contorta and western larch were common at mid-altitudes.

Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Study area
  5. Methods
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References

field trials

We evaluated the effects of habitat variables on GPS collar performance by stratifying the study area by percentage canopy cover and terrain obstruction, and by placing GPS collars at test sites. The variables, canopy cover and terrain obstruction, were queried using the GIS software ArcView 3·3 and Arcmap 8 (ESRI, Redlands, CA). The GIS layer for canopy cover was created from a cloud-free July 2003 Landsat 7 Enhanced Thematic Mapper (ETM+) satellite image with 30-m resolution using the Environment for Visualizing Images software program (ENVI; RSI Inc., Boulder, CO). A map of canopy cover was created by using linear spectral unmixing to identify the relative composition of three principal cover types (conifer trees, bare soil and grass/forbs/deciduous shrubs) within each pixel for the study area (Wessman, Bateson & Benning 1997).

Terrain obstruction was evaluated using ‘satellite view’ (Sager 2005), which is similar to the variables ‘available sky’ (D’Eon et al. 2002) and ‘visible sky’ (Frair et al. 2004). In contrast to the other variables that represented terrain obstruction, satellite view accounted for the fact that satellite coverage is greater on south-facing slopes than on north-facing slopes at northern latitudes. The satellite view program projected 48 points in the sky, which were weighted towards the southern hemisphere, and determined the number of satellite points that were obstructed by terrain features at locations on the ground (Sager 2005). Satellite view calculated a relative value of terrain obstruction for each 30 × 30-m grid cell within the study area. Low values of satellite view represented greater terrain obstruction, with a 100% satellite view representing no terrain obstruction. To ensure equal sampling across the range of all values, canopy cover and terrain obstruction were each stratified into four categories. Canopy cover was classified as: 1, 0–25%; 2, 26–50%; 3, 51–75%; 4, 76–100%. Previous studies have demonstrated a linear (Sager 2005) or curvilinear (D’Eon et al. 2002) relationship between terrain obstruction and GPS collar fix rate; therefore we stratified satellite view to sample adequately the end points of the distribution in our study area to maximize model fit. Satellite view was classified from best (least terrain obstruction) to worst (most terrain obstruction) satellite reception conditions: 1, 97–100%; 2, 91–96%; 3, 85–90%; 4, 77–84%.

To determine placement of test collars, canopy cover and satellite view were stratified into all 16 possible combinations, and each combination was sampled at three different locations within the study area, for a total of 48 test sites. We used a GIS database to buffer roads by 1 km and selected random points for each stratum within this buffer to identify test sites for placement of GPS collars.

We used 18 Lotek 3300 L GPS collars (2004/2005; Lotek Wireless, Ontario, Canada) programmed to record a location every 20 min for 24-h periods (72 attempted fixes per test site) during 23–30 May, 2–3 July and 4–5 August 2005. At each site, GPS collars were placed 1 m above the ground. For each test site, we estimated percentage canopy cover by averaging densiometer readings recorded in the four cardinal directions. For each attempted location fix, the collar recorded date and time, fix status (2-D or 3-D), number of satellites contacted, length of time the GPS unit was activated, and PDOP values. GPS locations were not differentially corrected. GPS collars recorded locations in latitude/longitude WGS84, which we transformed to UTM NAD 83 for analyses.

test collar data analysis

Location error was calculated by determining the Euclidean distance between each individual test location and the reference, or ‘true’, test collar location. The direction of location errors is randomly distributed around the true location (Moen et al. 1996) and reference locations can be estimated by averaging screened locations at a test site (Moen, Pastor & Cohen 1997; Dauwalter, Fisher & Belt 2006). For each test site, we recorded the true location as the geometric centre of the cloud of location data points, which was calculated by averaging all test locations with PDOP < 6. Using GIS software, the reference locations were evaluated visually in relation to the group of test locations to verify that reference locations were placed in the centre of the cloud of test locations. For location categories (PDOP values, 3-D, 2-D), we calculated the circular error probable (CEP), which determines the radius of a circle that incorporates the specified percentile of locations (Moen, Pastor & Cohen 1997; D’Eon & Delparte 2005). For example, a 95% CEP equal to 50 m would encompass 95% of test locations within a radius of 50 m around the reference location.

All possible models, including the continuous variables canopy cover, satellite view and their interaction, were evaluated using multiple linear regression (PROC REG; SAS Institute 2005). An arcsin square-root transformation was performed on the response variables, overall fix rate and 3-D fix rate, because they were proportional data (Zar 1999). The distribution of the location errors was heavily skewed to the right; therefore, to evaluate mean location error, a log transformation was performed to create a normal distribution. Reported mean location errors and associated standard deviations are back calculations of log-transformed location errors. Variables were assessed for correlation using Pearson's correlation matrix (r < 0·6 was considered uncorrelated) and normality and linear effects by evaluating residuals. Akaike's information criterion (AIC; Burnham & Anderson 1998) and R2 values were calculated for each model. We used the GPS collar test sites (n = 48) as the experimental units except when evaluating how location error related to PDOP values, for which we evaluated the relationships for both test sites and individual locations.

data screening options

We evaluated location errors and data reduction for four different data screening options: (i) screen all data collectively; (ii) screen only 2-D fixes; (iii) screen 3-D and 2-D fixes independently; and (iv) eliminate all 2-D fixes. For the first option, all locations with PDOP > 10 were removed from the data set (D’Eon & Delparte 2005). For the second option, we screened 2-D fixes at a PDOP > 5. For the third option, we screened 3-D fixes with PDOP > 10 and 2-D fixes with PDOP > 5 (Moen et al. 1996). For 2-D fixes, a PDOP of 5 was chosen as a PDOP value above which location accuracy became highly variable. These four data screening options were applied to location data sets from collars placed at 48 test sites and from 10 collars on free-ranging black bears. Black bears were captured during 2005 within the study area and fitted with Lotek 3300 L GPS collars programmed to record a location fix every 20 min between May and October. Field methods for capturing black bears were approved by the University of Idaho Animal Care and Use Committee (protocol 2005-27).

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Study area
  5. Methods
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References

The overall fix success for Lotek 3300 L GPS collars placed at test locations was high for all 16 habitat categories (mean 99·5%, SE 0·14, range 97·9–100%). Fix rate was not explained well by models that included habitat variables, canopy cover and satellite view (Table 1). However, missed fixes tended to occur in habitats with low satellite view, high canopy cover or a combination of both.

Table 1.  Models, with associated R2, AIC, ΔAIC values, and model parameter estimates evaluating how habitat variables affected overall fix rate, 3-D fix rate, location error and PDOP values for GPS collars at 48 test sites in the Purcell Mountains, Idaho, USA, 2005.
Dependent variable*Model variablesR2AICΔAICModel parameter estimates
  • *

    The GPS collar test site was used as the experimental unit (n = 48).

  • SV, satellite view; CC, % canopy cover; SV × CC, Interaction between SV and CC.

Fix rate (FR)SV0·065114·1210FR = 77·53 + 12·02 SV
SV CC0·096114·5360·415FR = 78·34 + 12·2 SV − 1·8 8 CC
CC0·028115·9761·855FR =−1·82 + 89·36 CC
SV CC SV × CC0·099116·3342·213FR = 73·90 + 13·17 SV + 6·68 CC − 9·40 SV × CC
3-D fix (3D)SV CC0·732184·54603D = 31·42 + 58·31 SV − 32·84 CC
SV CC SV × CC0·742184·6870·1413D = 3·74 + 88·72 SV + 20·60 CC − 58·66 SV × CC
CC0·626198·51113·9653D = 84·10 − 32·54 CC
SV0·095240·93656·3903D = 17·32 + 55·07 SV
Location error (LE)SV CC0·582254·8200LE = 61·26 − 62·46 SV + 50·00 CC
SV CC SV × CC0·582255·8190·999LE = 62·76 − 64·11 SV + 47·10 CC + 3·18 SV × CC
CC0·537256·6961·876LE = 4·82 + 49·68 CC
SV0·038291·80136·981LE = 82·73 − 57·54 SV
PDOP (PD)SV CC SV × CC0·519−12·7840PD = 16·88 − 14·54 SV − 16·06 CC + 20·05 SV × CC
SV CC0·383−2·8559·929PD = 7·42 − 4·15 SV + 2·21 CC
CC0·322−0·32512·459PD = 3·67 + 2·18 CC
SV0·05515·59928·383PD = 8·37 – 3·93 SV

Both canopy cover and satellite view were good predictors of the proportion of 3-D fixes, mean location error and mean PDOP values at the 48 test sites (Table 1). In all the top models, canopy cover contributed the greatest to model prediction (based on partial R2 values), although satellite view and the interaction of canopy cover and satellite view improved the models. As canopy cover increased and satellite view decreased, the proportion of 3-D fixes decreased, location error increased and PDOP values tended to increase.

The mean location error for all individual test locations sampled across the range of habitat variation was 14·3 m (SD 3·18 m), with a 95% CEP of 106·8 m (Table 2). Mean location error and CEPs for the total data set varied substantially within different habitat groups. For example, the 95% CEP increased from 19·9 m to 284·3 m when comparing sites with low canopy cover and low terrain obstruction to high canopy cover and high terrain obstruction; in addition, within these two groups the percentage of 3-D fixes decreased from 97·7% to 58·9%, respectively (Table 2). Furthermore, within signal quality categories (3-D, 2-D, PDOP < 10, PDOP < 6 and PDOP < 3), larger errors were observed as satellite view decreased and canopy cover increased. For example, for locations with PDOP < 10, the 95% CEP decreased from 246·0 m to 15·5 m when comparing sites with high canopy cover and high terrain obstruction to low canopy cover and low terrain obstruction (Table 2). Location errors for 2-D fixes (mean 36·0, SD 3·24, n= 613) were consistently larger than 3-D fixes (mean 11·8, SD 2·86, n= 2828) for all habitat categories (Table 2; F1,44 = 7·97, P= 0·0071).

Table 2.  GPS collar location errors based on location type and habitat for GPS collars placed at 48 test sites in the Purcell Mountains, Idaho, USA, 2005. For each location type, the first line includes all locations, with the second line representing the best satellite reception conditions, the third and fourth lines representing intermediate satellite reception conditions, and the last line representing the poorest satellite reception conditions.
Location typeHabitat* (SV–CC)Locations in group (%)Location error(m)
Mean (SD)50%95%99%100%
  • *

    First number indicates satellite view (SV) category; second number indicates percentage canopy cover (CC) category. SV measures terrain obstruction: 1 represents the best satellite view (97–100%) and 4 represents the worst satellite view (77–84%). For CC, 1 represents 0–25% and 4 represents 75–100%. Note that only a subsample of habitat categories is reported in this table.

  • Mean location error and associated standard deviations are back calculations of log-transformed location errors.

  • 50%, 95%, 99% and 100% CEP location errors. The CEP equals the radius of a circle that incorporates all location error values up to the specified percentile.

Total
 All10014·3 (3·18)13·8106·8262·6942·8
1–1100 5·0 (2·56) 5·0 19·9 90·2137·9
1–410024·0 (2·90)22·1187·8364·5772·1
4–110013·6 (2·58)12·4 74·5127·1367·5
4–410046·6 (2·68)51·5284·3587·9654·6
3-D
 All 82·211·8 (2·86)11·7 68·1131·6367·5
1–1 97·7 4·8 (2·50) 4·9 18·0 65·2137·9
1–4 74·918·7 (2·37)17·8 82·3187·8318·0
4–1 87·412·9 (2·52)11·2 71·1131·6367·5
4–4 58·936·7 (2·41)46·8126·3344·6344·6
2-D
 All 17·836·0 (3·24)35·1253·0565·9942·8
1–1  2·318·0 (3·28)14·3 90·2 90·2 90·2
1–4 25·150·7 (3·46)48·6364·5772·1772·1
4–1 12·619·1 (2·88)15·1 74·5 76·1 76·1
4–4 41·165·7 (2·84)57·4557·0654·6654·6
PDOP < 10
 All 92·512·8 (2·97)12·6 78·3201·7942·8
1–1 97·2 4·7 (2·40) 4·9 15·5 38·7 90·2
1–4 89·320·1 (2·54)18·6114·3261·3364·5
4–1 90·711·9 (2·40)10·8 60·0105·3367·5
4–4 85·141·2 (2·58)50·5246·0557·0587·9
PDOP < 6
 All 81·411·7 (2·88)11·7 68·6147·4587·9
1–1 93·5 4·6 (2·39) 4·8 15·0 38·7 90·2
1–4 79·118·1 (2·32)17·7 77·8143·0261·3
4–1 78·611·4 (2·31)10·6 58·6 83·4105·3
4–4 70·238·9 (2·67)48·9314·9587·9587·9
PDOP < 3
 All 32·1 9·5 (3·04) 8·9 65·5138·9383·3
1–1 59·7 4·2 (2·24) 4·5 14·3 29·1 32·7
1–4 21·919·9 (2·44)18·8101·0143·0143·0
4–1 24·710·5 (2·19) 9·6 48·6 74·5 74·5
4–4 30·535·8 (2·46)45·5 95·4321·9321·9

The relationship between PDOP and location error varied by habitat category and signal quality. There was a strong positive relationship between mean PDOP and mean location error at test sites (Fig. 1; R2 = 0·779, n= 48) and a weak relationship between PDOP and location error for all individual locations (R2 = 0·143, n= 3441). When individual location errors and PDOP values were evaluated based on signal quality (2-D vs. 3-D), the R2 values increased slightly and revealed differing slopes (Fig. 2). Also, a greater number of large outlier location errors were observed for 2-D locations compared with 3-D locations.

image

Figure 1. Relationship between mean LN location error and mean PDOP for 48 test sites in the Purcell Mountains, Idaho, USA, 2005.

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image

Figure 2. Relationship between location error and PDOP values for all individual (a) 2-D locations (n = 613) and (b) 3-D locations (n = 2828) in the Purcell Mountains, Idaho, USA, 2005.

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The application of the four data screening options to GPS location data sets from test collars and free-ranging black bears resulted in varying amounts of data reduction and accuracy. For the test collar data set, the first option (screen all locations with PDOP > 10) resulted in the elimination of 7·5% of locations, failed to purge several large location errors, and resulted in a 95% CEP of 78·3 m (Table 3). Under the second option (screen 2-D fixes with PDOP > 5), the majority of large outlier location errors were purged from the data set, 4·5% of locations were eliminated, and the 95% CEP equalled 79·2 m. The third option (screen 3-D fixes with PDOP > 10 and 2-D fixes with PDOP > 5) also removed most large outlier locations, eliminated 10·4% of locations and resulted in a 95% CEP of 69·4 m. The fourth option (screen all 2-D fixes) removed the most large location errors and eliminated 17·8% of locations, with the 95% CEP equal to 68·1 m. The four data screening options resulted in similar mean location errors for the test collar data set, ranging from 11·8 m to 13·2 m (Table 3). However, the percentage of accurate locations (location errors ≤ 100 m) eliminated varied substantially from 2·0% under option 2 to 14·9% under option 4 (Table 3). The percentage of all locations with errors > 100 m that were eliminated from the data set for options 1, 2, 3 and 4 were 38·7%, 43·3%, 61·3% and 67·0%, respectively. Elimination of large errors (> 300 m) followed a similar pattern (Table 3).

Table 3.  Data reduction and location accuracy resulting from four data-screening options for 48 test collar sites in the Purcell Mountains, Idaho, USA, 2005.
Data-Screening optionsTotal data retention (%)% of eliminated locations with errors > 100 m% of all locations with errors > 300 m eliminated% of all locations with errors 100 m eliminatedLocation error (m)
Mean error (SD)*50%95%99%100%
  • *

    Mean location error and associated SD are back calculations of log-transformed location errors.

  • 50%, 95%, 99% and 100% CEP location errors. The CEP equals the radius of a circle that incorporates all location error values up to the specified percentile.

Original data99·5NANANA14·3 (3·18)13·8106·8262·6942·8
1 (all PDOP > 10)92·530·151·9 5·312·8 (2·97)12·6 78·3201·7942·8
2 (2-D PDOP > 5)95·556·463·0 2·013·2 (2·96)13·0 79·2164·0587·8
3 (3-D PDOP > 10, 2-D PDOP > 5)89·634·570·4 7·012·2 (2·85)12·2 69·4138·5587·8
4 (all 2-D)82·221·285·214·911·8 (2·86)11·7 68·1131·6367·5

The GPS collar fix rate for 10 free-ranging black bears averaged 92·1%, ranging from 88·7% to 95·8% (Table 4). The mean fix rate for black bear GPS collars was 7·3% lower than for stationary test collars placed in forest with > 50% canopy cover, indicating that animal behaviour affected fix rate. For black bear data sets, data screening options 1 and 2 resulted in similar amounts of total data retention, 84·6% and 84·2%, respectively, with options 3 and 4 exhibiting greater data reduction, of 80·0% and 61·0%, respectively (Table 4).

Table 4.  Reduction of location data resulting from four data-screening options for 10 black bear GPS collar data sets in the Purcell Mountains, Idaho, USA, 2005.
Black bear individualsGPS collar fix rate (%)Data screening options
Option 1 All PDOP > 10Option 2 2-D PDOP > 5Option 3 3-D PDOP > 10, 2-D PDOP > 5Option 4 All 2-D
Screening* data loss (%)Total data retention (%)Screening* data loss (%)Total data retention (%)Screening* data loss (%)Total data retention (%)Screening* data loss (%)Total data retention (%)
  • *

    The percentage of location data eliminated from successful fixes.

  • The percentage of remaining locations once unsuccessful fixes and data screening have been accounted for.

195·87·089·2 6·389·811·085·326·370·7
291·37·784·2 9·183·013·279·335·858·6
395·28·487·1 7·887·713·382·527·968·6
491·28·783·3 8·783·313·678·932·361·8
588·78·581·2 9·980·014·675·835·657·1
692·98·485·2 7·985·712·781·138·557·2
789·07·682·2 9·680·513·477·141·352·3
892·68·784·5 9·383·913·979·939·955·6
990·78·782·710·181·615·276·938·555·8
1090·17·483·5 7·583·412·079·332·261·2
Mean (SD)91·8 (2·39)8·1 (0·63)84·3 (2·38) 8·6 (1·22)83·9 (3·09)13·3 (1·21)79·6 (2·83)34·8 (5·06)59·9 (5·85)

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Study area
  5. Methods
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References

The GPS collar fix rate for test collars was close to 100% within all habitats in our study area. Although the fix rate was high, missed fixes most often occurred at test sites with relatively high terrain obstruction, high canopy cover or a combination of both. Therefore, study areas with greater terrain obstruction and consistently high canopy cover may result in a higher proportion of missed fixes. In addition, greater terrain obstruction could potentially lead to greater location error because collars would receive location signals from, on average, fewer satellites that exhibit poorer satellite configuration, thus increasing PDOP values and the number of 2-D fixes.

This is the first study to report how location errors and PDOP values are affected by continuous ranges of canopy cover and terrain obstruction simultaneously. Our work expands on that of D’Eon & Delparte (2005), who evaluated the relationship between location error and PDOP values for GPS collars in the Selkirk Mountains of south-eastern British Columbia. The authors placed GPS collars in an open, grassy field with 0% canopy cover and little terrain obstruction and recorded a 95% CEP of 28·9 m; however, they acknowledged that higher canopy cover and terrain obstruction values may further influence location error. Our results demonstrate that both of these habitat variables can contribute significantly to PDOP values and location error. We report a 95% CEP of 106·8 m for all our locations, which sampled a range of terrain obstruction and canopy cover values. When evaluating our habitat category with the lowest terrain obstruction and lowest canopy cover, the 95% CEP equalled 19·9 m, which is comparable to D’Eon & Delparte's (2005) study.

Our GPS collar tests revealed more outlier location errors than previously reported. From 3441 test locations, we recorded 27 locations with errors > 300 m, compared to two outliers with errors > 300 m out of 6359 locations (D’Eon & Delparte 2005). These results can probably be attributed to our study sampling a range of canopy cover and terrain obstruction values. Although GPS collar performance can vary considerably among collar manufacturers (Frair et al. 2004), we believe that our results using Lotek collars are comparable with D’Eon & Delparte's (2005) study, which used Advanced Telemetry System (ATS, Isanti, MN) GPS collars, because previous research has shown Lotek and ATS collars to exhibit similar fix rates and location errors (Di Orio, Callas & Schaefer 2003; Frair et al. 2004).

Our study demonstrates that location errors, and possibly fix rates, for GPS collars should be quantified and reported in relation to specific habitats. For example, when evaluating location error the 95% CEP for the entire test collar data set ranged from 19·9 m to 284·3 m between the best and worst habitat categories for satellite reception, respectively. Evaluation of location error in areas with very low canopy cover and little terrain obstruction should be considered ideal conditions; extrapolation of results from tests conducted at such sites to habitats with higher canopy cover and terrain obstruction is inappropriate and will underestimate true location errors.

To screen GPS location data, a PDOP cut-off may effectively remove large location errors and subsequently increase accuracy of location data sets (Moen et al. 1996; Dussault et al. 2001; D’Eon & Delparte 2005). Two-dimensional fixes are less accurate than 3-D fixes, and demonstrate differing slopes when related to PDOP values (Fig. 2; Moen et al. 1996); therefore, it is appropriate to treat these two categories individually for screening. When screening data, the mean location error generally is not greatly improved; however, large location errors may effectively be eliminated from data sets, often decreasing CEP values (Tables 2 and 3; D’Eon & Delparte 2005).

When screening location data, there is a trade-off between data reduction and data accuracy. For our first option (screening all data collectively), a PDOP cut-off of 10 has been recommended to improve location accuracy and remove unusually large location errors, while only eliminating 1·3% of original locations (D’Eon & Delparte 2005). However, using this recommendation with our test collar and black bear data sets we eliminated 7·5% and 8·1% of locations, respectively, and still retained several large location errors. While the mean location error was similar for all screening options, the CEPs were most affected by screening 2-D fixes (options 2, 3 and 4). Because the majority of large location errors were associated with 2-D fixes with PDOP values > 5 (Fig. 2), removing these fix types resulted in the elimination of most outlier location errors and reduced CEP. However, data reduction varied considerably for options screening 2-D fixes, ranging from 8·6% to 34·8% for black bear data sets.

We believe that the most effective data screening approach is to focus on screening out 2-D locations at a specific PDOP cut-off. For our data sets, the second option we evaluated (screening 2-D locations with a PDOP > 5) was a suitable compromise between reducing large location errors and minimizing data reduction. The second option was most efficient in eliminating location errors > 100 m, eliminated only a small fraction of locations with errors ≤ 100 m, and resulted in total data retention of about 84% for black bear data sets. To increase the accuracy of location data further, a reasonable consideration would entail eliminating 3-D locations with high PDOP values (option 3), although data loss may be relatively large. Removing all 2-D locations resulted in the most accurate data set; however, data reduction was also the most substantial and probably resulted in an unjustifiable amount of data reduction. To determine an appropriate PDOP threshold value for data screening, the proportion of locations with relatively large errors could be evaluated across the range of PDOP values to assist in identifying acceptable levels of location error and data reduction.

Misplaced locations can bias analyses of habitat selection and space use, which has been evaluated in VHF telemetry (Nams 1989; Samuel & Kenow 1992; Withey, Bloxton & Marzluff 2001) and should also be addressed for GPS telemetry locations. To reduce the bias in identifying areas used by animals for habitat analyses, buffers could be applied to locations that are related to the magnitude of the location error (Rettie & McLoughlin 1999). Because PDOP values are related to location error for GPS telemetry, they could be used to set buffer sizes. In addition, while data screening may effectively remove large location errors from GPS telemetry data sets, it could potentially introduce biases that affect estimates of habitat and space use by eliminating locations associated with habitats that induce greater errors. Correction factors may be developed and applied to screened data sets to address this potential bias. Studies have examined how to correct for biases associated with missed GPS fixes based on habitat characteristics and space use (Frair et al. 2004; Horne, Garton & Sager 2007) and similar approaches may be appropriate for data sets that are screened to remove large location errors.

As canopy cover and terrain obstruction increases location data are more likely to exhibit higher PDOP values and fewer 3-D fixes, both of which indicate greater location error. Therefore the performance of GPS collars is largely dependent on the habitats that collared animals use. For example, greater location errors would be expected for animals that spend time in closed-canopy forests compared with more open environments. In addition to habitat variables, animal behaviour can significantly affect GPS collar performance. Our study has demonstrated that animal behaviour can reduce fix rates by as much as 11%. Future research evaluating relationships between GPS collar performance, animal behaviour and whether specific animal activities occur in different habitats could be used to develop correction factors for missed fixes and location errors in relation to animal behaviour and habitat selection.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Study area
  5. Methods
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References

Funding and support for this project were provided by the Idaho Department of Transportation, Idaho Department of Fish and Game, US Forest Service and the University of Idaho. We thank E. Strand for assistance with GIS analysis. We also thank R. D’Eon, J. Frair, H. Beyer, G. Smith, K. Sager and R. Hoffman for discussions and assistance regarding the evaluation of GPS radio collar performance. W. Wakkinen, J. Hayden, T. Johnson, T. Radandt and W. Kasworm assisted with field work. W. Estes-Zumpf, R. Long, J. Muir, D. Sanchez, W. Wakkinen, P. Zager and two anonymous referees provided helpful comments on this manuscript.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Study area
  5. Methods
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References
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