Dataset Identification:
Resource Abstract:
- description: Studies utilizing Global Positioning System (GPS) telemetry rarely result in 100% fix success rates (FSR). Many
assessments of wildlife resource use do not account for missing data, either assuming data loss is random or because a lack
of practical treatment for systematic data loss. Several studies have explored how the environment, technological features,
and animal behavior influence rates of missing data in GPS telemetry, but previous spatially explicit models developed to
correct for sampling bias have been specified to small study areas, on a small range of data loss, or to be species-specific,
limiting their general utility. Here we explore environmental effects on GPS fix acquisition rates across a wide range of
environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat use. We
also evaluate patterns in missing data that relate to potential animal activities that change the orientation of the antennae
and characterize home-range probability of GPS detection for 4 focal species; cougars (Puma concolor), desert bighorn sheep
(Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Part 1, Positive
Openness Raster (raster dataset): Openness is an angular measure of the relationship between surface relief and horizontal
distance. For angles less than 90 degrees it is equivalent to the internal angle of a cone with its apex at a DEM location,
and is constrained by neighboring elevations within a specified radial distance. 480 meter search radius was used for this
calculation of positive openness. Openness incorporates the terrain line-of-sight or viewshed concept and is calculated from
multiple zenith and nadir angles-here along eight azimuths. Positive openness measures openness above the surface, with high
values for convex forms and low values for concave forms (Yokoyama et al. 2002). We calculated positive openness using a custom
python script, following the methods of Yokoyama et. al (2002) using a USGS National Elevation Dataset as input. Part 2, Northern
Arizona GPS Test Collar (csv): Bias correction in GPS telemetry data-sets requires a strong understanding of the mechanisms
that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive
model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental features
that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success
rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two independent
data-sets from stationary test collars of different make/model, fix interval programming, and placed at different study sites.
No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors
have minor influence on the models ability to predict FSR in new study areas in the southwestern US. The model training data
are provided here for fix attempts by hour. This table can be linked with the site location shapefile using the site field.
Part 3, Probability Raster (raster dataset): Bias correction in GPS telemetry datasets requires a strong understanding of
the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive
a predictive model of fix aquistion. We found terrain exposure and tall overstory vegetation are the primary environmental
features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted
fix success rates (FSR) and showed little bias in predictions. The models predictive ability was evaluated using two independent
datasets from stationary test collars of different make/model, fix interval programing, and placed at different study sites.
No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors
have minor influence on the models ability to predict FSR in new study areas in the southwestern US. We evaluated GPS telemetry
datasets by comparing the mean probability of a successful GPS fix across study animals home-ranges, to the actual observed
FSR of GPS downloaded deployed collars on cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain
elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Comparing the mean probability of acquisition within
study animals home-ranges and observed FSRs of GPS downloaded collars resulted in a approximatly 1:1 linear relationship with
an r-sq= 0.68. Part 4, GPS Test Collar Sites (shapefile): Bias correction in GPS telemetry data-sets requires a strong understanding
of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to
derive a predictive model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental
features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted
fix success rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two
independent data-sets from stationary test collars of different make/model, fix interval programming, and placed at different
study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological
factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. Part 5, Cougar
Home Ranges (shapefile): Cougar home-ranges were calculated to compare the mean probability of a GPS fix acquisition across
the home-range to the actual fix success rate (FSR) of the collar as a means for evaluating if characteristics of an animals
home-range have an effect on observed FSR. We estimated home-ranges using the Local Convex Hull (LoCoH) method using the 90th
isopleth. Data obtained from GPS download of retrieved units were only used. Satellite delivered data was omitted from the
analysis for animals where the collar was lost or damaged because satellite delivery tends to lose as additional 10% of data.
Comparisons with home-range mean probability of fix were also used as a reference for assessing if the frequency animals use
areas of low GPS acquisition rates may play a role in observed FSRs. Part 6, Cougar Fix Success Rate by Hour (csv): Cougar
GPS collar fix success varied by hour-of-day suggesting circadian rhythms with bouts of rest during daylight hours may change
the orientation of the GPS receiver affecting the ability to acquire fixes. Raw data of overall fix success rates (FSR) and
FSR by hour were used to predict relative reductions in FSR. Data only includes direct GPS download datasets. Satellite delivered
data was omitted from the analysis for animals where the collar was lost or damaged because satellite delivery tends to lose
approximately an additional 10% of data. Part 7, Openness Python Script version 2.0: This python script was used to calculate
positive openness using a 30 meter digital elevation model for a large geographic area in Arizona, California, Nevada and
Utah. A scientific research project used the script to explore environmental effects on GPS fix acquisition rates across a
wide range of environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat
use.; abstract: Studies utilizing Global Positioning System (GPS) telemetry rarely result in 100% fix success rates (FSR).
Many assessments of wildlife resource use do not account for missing data, either assuming data loss is random or because
a lack of practical treatment for systematic data loss. Several studies have explored how the environment, technological features,
and animal behavior influence rates of missing data in GPS telemetry, but previous spatially explicit models developed to
correct for sampling bias have been specified to small study areas, on a small range of data loss, or to be species-specific,
limiting their general utility. Here we explore environmental effects on GPS fix acquisition rates across a wide range of
environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat use. We
also evaluate patterns in missing data that relate to potential animal activities that change the orientation of the antennae
and characterize home-range probability of GPS detection for 4 focal species; cougars (Puma concolor), desert bighorn sheep
(Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Part 1, Positive
Openness Raster (raster dataset): Openness is an angular measure of the relationship between surface relief and horizontal
distance. For angles less than 90 degrees it is equivalent to the internal angle of a cone with its apex at a DEM location,
and is constrained by neighboring elevations within a specified radial distance. 480 meter search radius was used for this
calculation of positive openness. Openness incorporates the terrain line-of-sight or viewshed concept and is calculated from
multiple zenith and nadir angles-here along eight azimuths. Positive openness measures openness above the surface, with high
values for convex forms and low values for concave forms (Yokoyama et al. 2002). We calculated positive openness using a custom
python script, following the methods of Yokoyama et. al (2002) using a USGS National Elevation Dataset as input. Part 2, Northern
Arizona GPS Test Collar (csv): Bias correction in GPS telemetry data-sets requires a strong understanding of the mechanisms
that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive
model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental features
that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success
rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two independent
data-sets from stationary test collars of different make/model, fix interval programming, and placed at different study sites.
No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors
have minor influence on the models ability to predict FSR in new study areas in the southwestern US. The model training data
are provided here for fix attempts by hour. This table can be linked with the site location shapefile using the site field.
Part 3, Probability Raster (raster dataset): Bias correction in GPS telemetry datasets requires a strong understanding of
the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive
a predictive model of fix aquistion. We found terrain exposure and tall overstory vegetation are the primary environmental
features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted
fix success rates (FSR) and showed little bias in predictions. The models predictive ability was evaluated using two independent
datasets from stationary test collars of different make/model, fix interval programing, and placed at different study sites.
No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors
have minor influence on the models ability to predict FSR in new study areas in the southwestern US. We evaluated GPS telemetry
datasets by comparing the mean probability of a successful GPS fix across study animals home-ranges, to the actual observed
FSR of GPS downloaded deployed collars on cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain
elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Comparing the mean probability of acquisition within
study animals home-ranges and observed FSRs of GPS downloaded collars resulted in a approximatly 1:1 linear relationship with
an r-sq= 0.68. Part 4, GPS Test Collar Sites (shapefile): Bias correction in GPS telemetry data-sets requires a strong understanding
of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to
derive a predictive model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental
features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted
fix success rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two
independent data-sets from stationary test collars of different make/model, fix interval programming, and placed at different
study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological
factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. Part 5, Cougar
Home Ranges (shapefile): Cougar home-ranges were calculated to compare the mean probability of a GPS fix acquisition across
the home-range to the actual fix success rate (FSR) of the collar as a means for evaluating if characteristics of an animals
home-range have an effect on observed FSR. We estimated home-ranges using the Local Convex Hull (LoCoH) method using the 90th
isopleth. Data obtained from GPS download of retrieved units were only used. Satellite delivered data was omitted from the
analysis for animals where the collar was lost or damaged because satellite delivery tends to lose as additional 10% of data.
Comparisons with home-range mean probability of fix were also used as a reference for assessing if the frequency animals use
areas of low GPS acquisition rates may play a role in observed FSRs. Part 6, Cougar Fix Success Rate by Hour (csv): Cougar
GPS collar fix success varied by hour-of-day suggesting circadian rhythms with bouts of rest during daylight hours may change
the orientation of the GPS receiver affecting the ability to acquire fixes. Raw data of overall fix success rates (FSR) and
FSR by hour were used to predict relative reductions in FSR. Data only includes direct GPS download datasets. Satellite delivered
data was omitted from the analysis for animals where the collar was lost or damaged because satellite delivery tends to lose
approximately an additional 10% of data. Part 7, Openness Python Script version 2.0: This python script was used to calculate
positive openness using a 30 meter digital elevation model for a large geographic area in Arizona, California, Nevada and
Utah. A scientific research project used the script to explore environmental effects on GPS fix acquisition rates across a
wide range of environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat
use.
Citation
- Title Variable Terrestrial GPS Telemetry Detection Rates: Parts 1 - 7 Data.
-
- creation Date
2018-05-20T18:33:17.773152
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- URL: https://doi.org/10.5066/F7PG1PT2
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- URL: http://dx.doi.org/10.1002/wsb.758
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Metadata data stamp:
2018-08-06T22:47:23Z
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on a transform by Damian Ulbricht. Run on 2018-08-06T22:47:23Z
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