Appendix S3: Covariates used in regression models and description of their derivation
Natural gas well pads
To obtain information on natural gas activity we downloaded publicly available data from the
Colorado Oil and Gas Conservation Commission website (http://cogcc.state.co.us/). These data
provide the location of every well drilled in the state, the current status of each well, the dates
drilling began (spud date), the date that drilling reached its deepest depth (total depth date), and
the date that the well was completed (the test date). We first categorized each well into one of 4
classes for every day between March 1, 2010 and December 1, 2013. Wells were classified as
drilling on every day between one week prior to the spud date and one week after the total depth
date (one week was an arbitrary time added to account for moving the substantial amount of
equipment required for drilling onto and off of the pad). Wells were classified as being between
the drilling phase and producing phase on days between one week after the total depth date and
the test date. Wells were classified as producing on days after the test date until the well was
listed as abandoned. Wells were listed as abandoned from the time their status was listed as
abandoned. In addition to these four statuses, the COGCC database includes a number of records
for permitted locations that were never drilled. To ensure that these classifications were accurate
we overlaid the well data with aerial imagery from the National Agriculture Imagery Program
(NAIP) to assess if these records were indeed abandoned locations or if there was evidence of
disturbance. We next overlaid all remaining records that were classified as abandoned,
producing, drilling or in the completion phase with the NAIP imagery to group wells onto well
pads. We then classified each well pad by the status of the well undergoing the most intensive
process for every day of the study period. Thus, a pad was only classified as producing if all
wells on the pad were producing or abandoned, was classified as between drilling and producing
if any wells were in this phase and all other wells were either producing or abandoned, and was
classified as drilling if any wells were being drilled.
Snow depth
We predicted snow depth using a spatially distributed snow-evolution modelling system
designed for fine spatial and temporal scale snow modelling, called SnowModel (Liston and
Elder 2006). This model takes inputs of land cover type, elevation, latitude, temperature, relative
humidity, precipitation, wind speed and direction and can predict snow depth at time scales as
fine as 10 minutes, and spatial scales as small as 1 m. This model accounts for numerous factors
influencing snow depth, including sublimation, redistribution from blowing snow, forest canopy
interception, snow density evolution, and snowpack melt (Liston and Elder 2006). We obtained
freely available meteorological data from 14 weather stations near our study area (data obtained
from http://www.nohrsc.noaa.gov/interactive/html/map.html and
http://www.wcc.nrcs.usda.gov/snotel/Colorado/colorado.html). We used these data to predict
snow depth at a daily time scale over a 30 × 30 m cell size between October 1 and May 31 of
every year of the sampling period. During the first two years of the study (winters 2011 and
2012) we placed 4 measuring stakes at locations in the study area and opportunistically measured
the snow depth at these stakes. During winter 2013 we deployed two weather stations equipped
with ultrasonic depth sensors (Judd Communications LLC, Salt Lake City UT, USA) which
provided daily snow depth measurements. The snow stake and ultrasonic depth measurements
were used to assess the performance of the SnowModel and to adjust input values of
precipitation to best match on-the-ground measurements. After adjustment of input values,
modelled snow depths for each year were highly congruent with on-the-ground measurements
(linear regression models comparing observed to modelled estimates all indicated R2 values >
0.8).
Road network
To characterize the road network we digitized all roads in the study area using the NAIP imagery
from both 2011 and 2013. There were few new roads built in the area between these years, and
with no imagery available in 2012 we chose to create a single road network layer representing
conditions during the summer of 2013. This area receives little traffic other than that associated
with natural gas development, though during the fall hunting seasons (September through
November) traffic increases. Thus we further classified the road network into primary and
secondary roads. We first classified all paved roads and all roads leading to well pads or facilities
as primary roads, while classifying all roads that were two-tracks or trails as secondary roads.
This classification accounted for the majority of roads in the area. The remaining roads were
classified as either primary or secondary based on our knowledge of the study area and their
appearance in the NAIP imagery (i.e. roads that dead-ended with no industry infrastructure
associated with them were characterized as secondary roads).
Notes on derivation of dynamic covariates
For NDVI, snow and body fat covariates, the absolute differences between the two years of
interest were calculated (assessing if the magnitude of differences in these variables explained
changes in range use). For the development covariates, the difference between the second and
first year was calculated in order to preserve the direction of the change (i.e., an increase or
decrease).
Table S3.1. Names, descriptions, sources, pixel size (when available), and the unit of time over which the covariates were available for covariates
used in regression models examining range size and bi-annual range overlap for summer and winter ranges of female mule deer in the Piceance
Basin of Northwest Colorado, USA.
Covariate
Description
Source
Pixel size
Time scale
Proportion of range comprised of treed land cover
Colorado Vegetation Classification Project
25 m × 25 m
NA
Environmental
tree
(http://ndis.nrel.colostate.edu/coveg/)
TRI
Terrain ruggedness index. Squared difference between
DEM from http://seamless.usgs.gov
30 m × 30 m
Normalized difference vegetation index (NDVI) averaged
Calculated from layers available at
1 km × 1 km
Every 10 days
over area and time period of range
http://www.vito-eodata.be/
Maximum value of NDVI averaged over the area of the
Calculated from layers available at
1 km × 1 km
Every 10 days
range for every 10 day period
http://www.vito-eodata.be/
Total winter snow fall summed for each pixel and
See supplemental information
30 × 30 m
Daily
See supplemental information
30 × 30 m
Daily
elevation in each cell and 8 neighbours averaged over
entire range
avg_NDVI
peak_NDVI
snow_total
averaged over entire range
snow_avg
Average winter snow fall per pixel and averaged over
entire range
Anthropogenic
rd_dens_all
Density of all roads within range
See supplemental information
NA
NA
rd_dens_major
Density of all primary roads within range
See supplemental information
NA
NA
dens_prod
Density of well pads with producing wells only
See supplemental information
NA
Daily
dens_drill
Density of well pads with wells being actively drilled
See supplemental information
NA
Daily
dens_pipe
Density of pipelines
Bureau of Land Management
NA
Annual
dens_fac
Density of compressor stations, natural gas plants, and
See supplemental information
NA
Annual
other industrial facilities
Individual
fat
Percent ingesta-free body fat
Measured during capture
NA
Annual
Age
Age of deer at capture
Measured during capture
NA
Annuala
a
While age varied by year, when assessing overlap a difference in age was not calculated. Rather the age covariate was calculated as the average
age between the two years of interest.
References
Anderson CRJ, Bishop CJ (2012) Population performance of Piceance Basin mule deer in
response to natural gas resource extraction and mitigation efforts to address human
activity and habitat degradation Job Progress Report. Colorado Division of Wildlife, Fort
Collins, CO, USA
Anderson Jr. CR (2014) Population performance of the Piceance basin mule deer in response to
natural gas resource extravtion and mitigation efforts to address human activity and
habitat degredation Job Progress Report. Colorado Parks and Wildlife, Fort Collins, CO
Liston GE, Elder K (2006) A distributed snow-evolution modeling system (SnowModel). J
Hydrometeorol 7:1259-1276. doi: Doi 10.1175/Jhm548.1