Impact of Habitat-Specific GPS Positional Error on Detection of Movement Scales by First-Passage Time Analysis
Porter WF (2012) Impact of Habitat-Specific GPS Positional Error on Detection of Movement Scales by First-Passage Time
Analysis. PLoS ONE 7(11): e48439. doi:10.1371/journal.pone.0048439
Impact of Habitat-Specific GPS Positional Error on Detection of Movement Scales by First-Passage Time Analysis
David M. Williams 0 1
Amy Dechen Quinn 0 1
William F. Porter 0 1
Cedric Sueur, Institut Pluridisciplinaire Hubert Curien, France
0 Current address: Department of Fisheries and Wildlife, Michigan State University , East Lansing, Michigan , United States of America
1 Department of Environmental and Forest Biology, State University of New York College of Environmental Science and Forestry , Syracuse, New York , United States of America
Advances in animal tracking technologies have reduced but not eliminated positional error. While aware of such inherent error, scientists often proceed with analyses that assume exact locations. The results of such analyses then represent one realization in a distribution of possible outcomes. Evaluating results within the context of that distribution can strengthen or weaken our confidence in conclusions drawn from the analysis in question. We evaluated the habitat-specific positional error of stationary GPS collars placed under a range of vegetation conditions that produced a gradient of canopy cover. We explored how variation of positional error in different vegetation cover types affects a researcher's ability to discern scales of movement in analyses of first-passage time for white-tailed deer (Odocoileus virginianus). We placed 11 GPS collars in 4 different vegetative canopy cover types classified as the proportion of cover above the collar (0-25%, 26-50%, 51-75%, and 76-100%). We simulated the effect of positional error on individual movement paths using cover-specific error distributions at each location. The different cover classes did not introduce any directional bias in positional observations (1 m#mean#6.51 m, 0.24#p#0.47), but the standard deviation of positional error of fixes increased significantly with increasing canopy cover class for the 0-25%, 26-50%, 51-75% classes (SD = 2.18 m, 3.07 m, and 4.61 m, respectively) and then leveled off in the 76-100% cover class (SD = 4.43 m). We then added cover-specific positional errors to individual deer movement paths and conducted first-passage time analyses on the noisy and original paths. First-passage time analyses were robust to habitat-specific error in a forest-agriculture landscape. For deer in a fragmented forest-agriculture environment, and species that move across similar geographic extents, we suggest that first-passage time analysis is robust with regard to positional errors.
Funding: Funding for this project was provided by the New York State Department of Environmental Conservation (NYSDEC) with partial support from the
United States Federal Aid in Wildlife Restoration Project W-173-G. Additional support was provided by the United States Geological Survey and the
McIntireStennis Foundation at SUNY-ESF. The NYSDEC assisted in the selection of the study areas and provided technicians to assist in deer-trapping efforts. The NYSDEC
had no role in additional data collection, analysis, decision to publish, or preparation of the manuscript. All other funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
Animal movement data are typically collected using very-high
frequency (VHF) radio telemetry, and more recently, global
positioning system (GPS) technology that record locations of
animal positions in space and time. GPS technology has several
advantages over VHF radio telemetry including finer spatial and
temporal resolutions of location data and an ability to obtain
positions remotely during harsh conditions and in hard-to-access
locations. Moreover, the increased precision and accuracy of GPS
tracking devices has allowed ecologists to evaluate animal
behaviors and habitat use at increasingly finer scales. However,
it is important to understand the limitations and biases of data
acquired using GPS collars and the degree to which that error
influences interpretations of data analyses.
For instance, as GPS collars attempt to acquire positional fixes
at scheduled intervals (fix schedule), variations in behavior of the
collared animal can affect the accuracy of the location [1,2,3,4].
Similarly, collar model and manufacturer have been shown to
influence accuracy and precision [4,5,6,7]. The impacts of terrain
on GPS collar positional error and bias are less clear, but appear to
be greatest when elevations and slope gradients are large . In
landscapes with less rugged topography, positional accuracy is
probably more affected by vegetative cover [9,10,11], and fix
schedule [12,13]. As a consequence of the positional errors arising
from these factors, the set of locations recorded by a GPS collar
represent one path among a distribution of paths that might have
been recorded for that animal, rather than representing the actual
path an animal travelled.
Movement analyses that do not account for uncertainty in GPS
locations or the consequent distribution of possible movement
paths, may lead to spurious results, insensitive analyses, or at best,
limit the applicability of analytical conclusions. Jerde and Visscher
 cautioned against modeling movement using step length and
turn angle distributions based upon movements that were small
relative to the standard deviation of positional error. Frair et al. 
demonstrated that vegetation-specific fix-rate bias of GPS collars
caused estimates of habitat selection parameters to fluctuate. Both
groups stressed the need to run multiple simulations to stabilize
those parameter estimates. Montgomery et al. [15,16] found that
telemetry positional error impacted the accuracy of resource use
characterization. Moser and Garton  found that estimates of
home range size using fixed kernel density estimators were unlikely
to be influenced by positional error given adequate sample sizes.
In simulations of movement, detection of movement scales was
influenced by observation rate, missed fixes, positional error, and
assumptions of movement responses to patches .
The first-passage time (FPT) analytical technique, which is
increasingly used to quantify scales of movements, may be
especially sensitive to error. FPT evaluates the variation of time
spent within a specific area, or evaluation extent, along individual
movement paths. Peaks in the variation of that passage time within
a range of extents identify the scales at which each movement path
is organized. These peaks have been interpreted as the landscape
scales to which individuals are responding [19,20]. Because small
changes in a single positional observation could, at least
hypothetically, determine whether an individual remains within
a given evaluation extent or exits it, FPT may be sensitive to
positional error. Additionally, the passage time of steps entering
and exiting those evaluation extents is determined by dividing the
length of those segments by the rate of travel for that step.
Positional error will alter the distance traveled between fixes and
thus affect inferences about the rate of travel for those path
White-tailed deer (Odocoileus virginianus) that occupy the
fragmented forest-agricultural landscape and rolling topography of
central New York State are excellent organisms with which to test
the influence of GPS location error on the detection of animal
movement patterns. Deer in central NY typically utilize 2 distinct
land-cover types: agriculture, which is open with little to no
overhead vegetative cover, and forest, which ranges from heavy to
light overhead vegetative cover . GPS positional error is
expected to be smaller in open areas than in heavy cover because
of a reduced chance of radio signals being obstructed by dense
canopy and a greater amount of sky available across which to
locate satellites nearer the horizon [5,7]. For similar reasons, GPS
bias (failure to obtain a positional fix) is also expected when collars
miss more fixes in heavy vegetative cover if they fail to receive
signals from 3 or more satellites within the time interval designated
to obtain a fix . Any analyses of the movement of deer,
particularly analyses that do not account for potentially distinct
differences in observation error may be suspect.
The central question we address here is how robust are FPT
analyses to positional error? Past research has investigated the
influence of error on FPT analysis using simulations of movement
in simulated landscapes [18,22]. However, the problem becomes
more challenging for a species like deer in a fragmented
forestagricultural landscape because they are moving between open
(presumably low error) and forested (presumably higher error)
habitat. The differences in error could have greater implications
for identifying the scales at which their movements are organized.
Thus, we sought to determine these influences using empirical
data describing actual animal movement paths, where positional
error is expected to differ along a path that passes through a
heterogeneous landscape. Our objectives were to evaluate the
influence of vegetation cover and fix schedule on positional error
in GPS data and then explore how variation of positional error in
different cover types would affect the ability to discern scales of
movement in analyses of first-passage time.
The study area included locations in Onondaga, Cortland,
Madison, and Oneida Counties of central New York State (Fig. 1).
Stationary collars were located within Spafford Township.
Landcover was a mix of forest (44%) and agriculture (34%) with small
communities (9% developed). Forests were dominated by
hardwoods, notably sugar and red maple (Acer saccharum and A. rubrum),
American beech (Fagus grandifolia), white ash (Fraxinus americana) and
black cherry (Prunus serotina). Conifer plantations originating in the
1930s were composed of white, red and Scotch pine (Pinus strobus,
P. resinosa, and P. sylvestris), and white and red spruce (Picea glauca,
P. rubens). Agricultural crops were mostly related to dairy and
include corn, winter wheat, oats, alfalfa, and soybeans. A rolling
topography occurred throughout those portions of the study area
in Onondaga, Cortland and Madison Counties; areas in Oneida
County are located on a glacial lake plain. Average temperatures
were 25.0uC during February and 20.6uC in July (19662006).
Elevations range from 93 m to 652 m and the region lies to the
south and east of Lake Ontario. The combination of the prevailing
wind patterns and elevation affects precipitation. Average total
annual precipitation was 97.3 cm/year (19662006). Winters are
variable with heavy snow events and frequent thaws. Snowfall
averaged 251 cm/year (19662006) and ranged from 241 cm/yr
to 336 cm/yr during this study . The deepest snowpack
(74 cm) during our study occurred in Oneida County in February
of 2007 . Road density in the region was 1.85 km/km2; 1.5%
of the landscape was .1.6 km from a road .
We placed 11 GPS collars (model GS2000, Advanced
Telemetry Systems, Inc.) in 4 different vegetative canopy cover
types classified as the proportion of cover above the collar (025%,
2650%, 5175%, and 76100%). All collars collected data from
12 February 2008 to 11 March 2008. Collars were affixed to
wooden stakes 1 m above ground level with the antenna oriented
upwards and programmed with a primary schedule that attempted
to acquire a position every 5 hr. Two of the collars in each cover
class also attempted to acquire positions every 30 min for a 2 day
period per 2 week interval. For the 025% cover class, we placed 3
collars in an agricultural field. For the 2650% and 5175% class,
we placed 4 collars in mixed hardwood/coniferous forest (2 collars
each). We placed the remaining 4 collars in a dense stand of
conifers where vegetative cover was 76100%. We used a
handheld GPS Trimble unit (GeoXH) to record the true position
of each collar as the mean of $25 fixes. We determined the
percentage of missed positional fixes in each cover type for both
the 5 hr schedule and 30 min schedule. We used the X and Y
positional distance between each acquired location and the true
position to evaluate potential positional bias and describe the
standard deviation of an assumed symmetrical distribution
representing the positional error for both axes of each fix-schedule
and cover type combination. We used an F-test to compare the
variances of the positional error distributions for each canopy
cover class. We conducted model selection using Akaikes
Information Criterion (AIC) to determine the best function
relating the standard deviation of error for each collar to the
percent canopy cover at each collars location. We selected from
null, linear, power, and logistic models to describe this relationship
(Table 1). We conducted leave-one-out cross validation to evaluate
the predictive accuracy of the best models. We identified the
number of missed fixes to quantify any habitat (canopy cover) bias
for acquisition of positional locations.
We used GPS collar (model GS2000, Advanced Telemetry
Systems, Inc.) data from 71 white-tailed deer (27 males and 44
females). Deer were captured during January-April 2006 and 2007
using modified Clover traps , rocket nets, and dart guns (State
University of New York College of Environmental Science and
Forestry Institutional Animal Care and Use Protocol no. 2005-1).
Collars were programmed to take a GPS location every 5 hr. A
secondary fix schedule acquired positions every 30 min for a 48-hr
period every 2 weeks. GPS locations were stored on board the
collars that were remotely detached from study animals and
retrieved after approximately 1 year (mean = 254 days).
We simulated the effect of positional error on individual
movement paths using the 5 hr fix schedule and cover- specific
error distributions at each location. Estimated percent canopy
cover at each observed deer location was extracted from the 2001
National Land Cover Tree Canopy Database for New York State
 (Fig. 1). In our study area, percentage canopy cover as defined
by this database was significantly spatially autocorrelated at lag
distances #250 m and highly autocorrelated (r.0.7) for lag
distances ,100 m. Because percentage canopy cover is highly
SDcanopy = b+a * canopy
SDcanopy = b+a * (canopyp)
SDcanopy = b
correlated at distances much greater than the average GPS
location error, we used the percentage canopy cover of the 30 m
cell underlying each observed GPS location. The best model
describing the relationship between canopy cover and positional
error was used to predict a site-specific error distribution at each
observed GPS location. We randomly selected an X and Y
distance from those distributions to relocate each observed point.
We produced 500 iterations of the movement path for each deer
using this habitat specific-positional error relationship.
First-passage time analyses
We conducted FPT analyses of each deer movement path using
the 5 hr fix schedule in the adehabitat package of the R [27,28].
We calculated the passage time spent moving along the path
within a circle of given radius that was centered on each GPS
location along an individual movement path. Where the circle
intersected the path between GPS locations we assumed constant
rates of travel along the corresponding step and calculated time
spent along the resulting path segment. We evaluated the passage
time along each path using circles with radii ranging from 25 to
10,000 m at 25 m intervals. We identified the degree of
aggregation of movements by the variation in passage time across
all circles of a given size along the path. Because mean passage
time and the corresponding variation around that mean are
expected to increase as a function of increasing circle size we
divided the variance in passage time by the area of the evaluation
scale (circle size). For each deer, we identified peaks in the variance
of passage time per unit area (varFPT/area) [19,20]. Because FPT
is an individual-based analysis, we identified common patterns in
deer movement and randomly selected individuals, from a set of
deer which exhibited those patterns, to identify the impact of
positional error on the identification of peaks at different spatial
scales. Once representative individuals were chosen, we repeated
the FPT analyses at 10 m intervals across a smaller range where
the peaks of interest occurred. Because positional error may be
exacerbated by changes in animal behavior or seasonal changes to
the canopy, we also evaluated the impact of greater positional
error on FPT interpretation by increasing the predicted standard
deviation of the observed error at each location by factors of 2, 5,
and 10. For the randomly selected deer, we conducted 500
iterations of the movement paths as affected by these 3 additional
We found that the standard deviation of positional error of fixes
acquired on a 5 hr schedule increased significantly with increasing
canopy cover class for the 025%, 2650%, 5175% classes
(SD = 2.18 m, 3.07 m, and 4.61 m, respectively), but that there
was no significant difference between the 5175% and 76100%
cover classes (4.61 m and 4.43 m, F306,259 = 0.92, p = 0.51; Fig. 2).
The relationship was similar for positions acquired on a 30 min
schedule where the standard deviation of the positional error for
the 025% (3.03 m), 2650% (4.47 m), and 76100% (5.29 m)
cover class differed significantly (p,0.001 for all comparisons)
(Fig. 2). For the 30-min fixes, the standard deviation of the
positional error for the 5175% canopy cover class (4.88 m) did
not differ significantly from the 2650% (F387,387 = 0.84, p = 0.085)
or 76100% classes (F484,387 = 1.17, p = 0.10).
The relationship between the standard deviation of positional
error for stationary collars and percentage canopy cover was best
described by the logistic function. This model had 71% of the
weight of evidence as the best model given the data and set of
models (Table 1). Model validation indicated this model was a
Figure 2. Positional error (m) of stationary collars placed in varying percentages of canopy cover in the Town of Spafford, New
York from mid-February to mid-March 2008 for two different fix schedules: a) 5 hr and b) 30 min. Dashed lines represent the 50% and
90% normal ellipse contours of position densities. Percent canopy cover increases from left to right (025%, 2650%, 5175%, 76100%).
good predictor of the standard deviation of positional error.
Observed and predicted positional error distributions were highly
correlated (rs = 0.89, p,0.001) and their regression resulted in a
slope near 1 (0.826, SE = 0.151)(Fig. 3).
We observed few missed positional fixes from the stationary
collars in all canopy cover classes. We observed a 100% success
rate for fixes acquired on a 30-min schedule regardless of canopy
cover. Collars programmed to record position every 5 hr achieved
100% (n = 283), 99.62% (n = 261), 98.85% (n = 259), and 96.74%
(n = 307) success rates for each cover class from least to greatest,
respectively. We observed sequential missed fixes on only one
occasion in the 76100% cover class when two fixes were missed.
Mean success rate for collars mounted on study animals was 86%.
White-tailed deer in central New York exhibited consistent
scales of movement along their movement paths. FPT analysis for
each individual across all seasons was dominated by a major peak
in varFPT/area at scales (radii) from 575 m to 1,675 m (Fig. 4;
). We observed additional lower magnitude peaks at larger
scales (3,000 m6,000 m) for 32% (n = 20) of individual deer
(Fig. 4). Twelve individuals (19%) exhibited high values of
varFPT/area at the smallest scales evaluated that declined with
increasing scale (25 m150 m)(Fig. 5). No peaks in varFPT/area
were observed for 8% (n = 5) of individuals.
Impact of error on FPT peaks
Scales of movement by deer as detected by first-passage time
analyses were minimally influenced by habitat-specific error along
the movement path. When we accounted for the observed
positional error (1X) the peak consistently occurred within 10 m
of the peak identified using the observed path. The varFPT/area
as a function of scale along the path for deer #9, an adult female,
was representative of analyses for many individuals exhibiting a
dominant peak in the 425 m to 1,675 m range. For this individual,
we found 73% of the iterations identified the detected scale as
860 m, and 99% of the iterations identified the peak in the range
850 m to 870 m (Fig. 6A). As we increased the standard deviation
of the error (2X and 5X) incorporated into the movement path, on
average the identified peaks remained consistent with the
empirical peak, but the variance of the corresponding distributions
increased. When we increased the observed habitat specific error
by a factor of 10, the mean observed peak occurred at 900 m
(SD = 40 m), a 40 m shift from the peak identified by the observed
path (Fig. 6A). We found 95% of the dominant peak locations
(centered on the mean) from the 10X error distributions ranged
from 830 m to 970 m. This range included the location of the
dominant peak in varFPT/area for the original path, although it is
was observed rarely. Fewer than 6% of the observations of a
normal distribution with a mean and standard deviation
equivalent to that described by the 10X error peak locations are
expected to fall between 855 m and 865 m, the location of
dominant peak for the original path without error added.
Large-scale peaks in varFPT/area were also robust to potential
habitat-specific positional error. The varFPT/area as a function of
scale along the path for deer #101, another adult female, was
representative of analyses for individuals exhibiting a large-scale
peak in the 3,000 m to 6,000 m range (Fig. 6B). When we
analyzed 500 paths resulting from incorporating the predicted
positional error (1X) into the original movement path, we found
199 (39.8%) of the peaks were located at a scale identical to the
one resulting from the original path (3440 m, Fig. 6B). The range
3430 m to 3450 m included 94.5% (n = 472) of the peak locations,
and all of the peaks occurred between 3400 m and 3450 m. As we
increased the amount of simulated error introduced to the
observed path (2X, 5X, 10X), we found the mean scale at which
the large-scale peak in varFPT/area occurred to differ by ,10 m.
The variation around that mean increased with increasing
positional error. We observed standard deviations of 9.2, 11.3,
19.5, and 31.5 m for path iterations using the 1X, 2X, 5X, and
10X error functions, respectively (Fig. 6B). Twelve percent of the
observations of a normal distribution with a mean and standard
deviation equivalent to that described by the 10X error peak
locations are expected to fall between 3,435 m and 3,445 m (the
location of large-scale peak for the observed path). Fifty-five
percent of the observations are expected to occur within a 50 m
interval centered on 3440 m.
For all instances of peaks occurring at small scales we found that
the peak identified using the observed path persisted despite the
incorporation of positional error. The varFPT/area as a function
of scale along the path for deer #68, an adult female, was
representative of analyses for individuals exhibiting a small-scale
Figure 4. Plots of area adjusted variance in first-passage time (varFPT/area, s2/m2) as a function of the scale used to determine
passage time along the observed movement path (radius of circle, m) for 4 white-tailed deer in central New York. Peaks in varFPT/area
indicate the landscape scale(s) to which individuals are responding by altering time spent along their movement path.
Figure 5. Plots of area adjusted variance in first-passage time (varFPT/area, s2/m2) as a function of the extent used to determine
passage time along the observed movement path (radius of circle, m) for 3 white-tailed deer in central New York. Peaks in varFPT/area
indicate the landscape scale(s) to which individuals are responding by altering time spent along their movement path.
peak at scales ,100 m (Fig. 6C). For positional error simulations
up to 5X the canopy-cover error function, nearly all iterations
displayed peaks at scales ,100 m (1X = 100%, 2X = 100%,
5X = 99.6%). When the canopy-cover error function was
increased by a factor of 10 (representing a range of standard
deviations from 25 m70 m), 11.2%
displayed no peak at scales ,100 m.
of the 500 iterations
Figure 6. Violin-box plots displaying the impact of habitat-specific positional error on medium-(A), large-(B), and small-scale (C)
peaks in varFPT/area for 500 simulated movement paths of white-tailed deer in central New York in 20062007. Dashed lines indicate
the location of the peak in varFPT/area for the observed path (resolution = 10 m).
While others have investigated the influence of various
behavioral, landscape, and error changes upon FPT analyses
using movement simulations and have evaluated the impact of
positional error of ARGOS collars on FPT for small samples of
albatross (Pinaud 2008), to our knowledge we are the first to
examine the potential impacts of error on FPT analyses among a
large number of GPS collared individuals in a natural landscape.
We found mean positional error of GPS collars was ,5 m across a
full range of canopy cover conditions for deer in the fragmented
forest-agricultural landscape of central New York. Few observed
deer movements were smaller than the observed positional error of
stationary collars (n = 794 of 71,095, 1.1%). This low positional
error, when viewed in a context of the geographic extent of the
movement we saw in deer, allowed for consistent estimates of the
peaks in FPT analysis at all scales.
While mean positional error was low regardless of cover type,
the standard deviation of the positional error of these collars was
directly related to percentage canopy cover. Thus the spread of the
potential locations around each observed location was greater as
canopy cover increased. When we modeled that relationship, the
best performing model was the logistic, suggesting that the
distribution of positional error changes rapidly from low to high
in corresponding canopy cover. Evaluation of the percentage
canopy cover NLCD data for our study area reveals that when
water is disregarded, 49% of the landscape is classified as having
#25% overhead cover and 40% is classified $75% . This
dichotomy suggests that collars on deer moving through this
landscape frequently function where canopy cover is associated
with the extremes of their error distributions.
The positional errors of collars deployed on study organisms are
potentially different from those identified by stationary collars.
Unlike stationary collars, one cannot easily know the true location
of each collared individual when GPS positions are recorded, and
cannot identify the positional error of the recorded path. However,
the difference in success rates of stationary versus deployed collars
may inform whether we expect those to differ. Because we
observed so few missed fixes among stationary collars, we did not
evaluate the impact of missed fixes upon FPT analyses. It is
important to note the difference between the success rate of
stationary collars and those mounted on deer. We observed high
success rates for acquiring positions among stationary collars
across all canopy cover classes. For collars programmed on the
30min fix schedule, this rate was 100% regardless of canopy cover;
the success rate for the 5-hr schedule was also high ranging from
97% to 100%. However, the success rate among collars mounted
on study animals (5-hr schedule) was lower (mean = 86%). This
difference suggests that factors other than canopy cover are
influencing the success rate of signal acquisition, and may imply
that positional error of mounted collars may be greater than that
observed among stationary collars.
Additional landscape characteristics may influence collar error
and explain differences between the fix success of stationary and
moving collars. Besides vegetation, topography has been found to
influence GPS error. Topography impacts GPS error in canyons
or mountainous terrain [4,6,12]. However, in less rugged terrain
topography is seldom included in models describing GPS error.
Dussault et al.  found no relationship between topographical
metrics and GPS error. Their study area in eastern Canada had an
elevation range of 250 to 1050 m. Our study area contained even
less variation in elevation (range = 93 m652 m, mean = 289 m,
SD = 145.5 m) and thus we assumed differences in elevation did
not impact collar error.
We suspect that the difference between the success rates of
stationary and deployed collars is primarily due to being mounted
on a moving organism where collar position varies with behavioral
changes. Collar position (offset from vertical) has been found to
negatively impact both position acquisition and location errors .
Researchers have found bedding behaviors in deer and moose to
result in reduced fix success [29,30]. Similarly, Graves and Waller
 observed increased fix success with increased movement rates.
More importantly they found that individual physical
characteristics of bears explained most of the variation in acquired/missed
GPS fixes. These studies demonstrate that collar performance is
related to individual animal characteristics and behaviors. While
these findings focus on missed fixes, it follows that positional error
may also be related to these variables. Unfortunately it is difficult
to know the true location of an individual at a specific time and
thus, few have investigated the impact of behavior and motion on
positional error. Cargnelutti et al.  evaluated GPS collar
performance on a moving organism and found no difference in the
positional error of stationary and moving collars, though stationary
collars were mounted on tripods rather than study animals.
While we are unable to say whether the positional error is also
greater for collars mounted on study organisms, it is likely given
the increase in missed position acquisitions. This is the primary
reason we evaluated the impact of positional error on FPT
analyses using not only the positional error distribution observed
among stationary collars, but also increasing multiples of the
standard deviation (2, 5, and 10). The fact that for deer, FPT
analyses were consistent even when positional error was assumed
to be much greater than observed among stationary collars,
indicates that even potentially unidentified sources of positional
error are unlikely to influence detection of the scales at which deer
movements are organized.
We found FPT analyses of movements for deer produced peaks
in varFPT/area at 3 different landscape scales (small,
intermediate, and large). We observed varFPT/area to be high at small
scales (,50 m) and decline rapidly as scale increased for 12
individuals. For all other individuals we were unable to identify
sufficient variation in varFPT/area to produce a peak at those
scales. This may be a result of the temporal resolution of our data
or variation among individuals. The 5-hr schedule for position
acquisition may be too infrequent to identify variation in time
spent along a path at scales ,100 m. Similar peaks in varFPT/
area were observed for individual elk (Cervis elaphus) in Alberta
. They interpreted these peaks as identifying the scale at which
resting behaviors were occurring, with a caution to the potential
impact of error on interpretation of the analyses at small scales.
These small-scale peaks may also be artifacts of the analytical
process . We found these high values in varFPT/area
occurring at small spatial scales to be consistent when we
iteratively applied habitat-specific positional error to the observed
movement path. Only upon incorporating error 10X greater than
that observed among the stationary collars did the signal at small
scales occasionally decline.
Using simulated movements in simulated landscapes, Pinaud
 developed a log-linear relationship between the positional
error and the smallest scales detectable by FPT analyses. The
positional error of our GPS collars was much smaller than the
Argos collars he was simulating, but if we extrapolate his findings
they suggest that when the standard deviation of positional error is
8 m (the largest we observed among stationary collars in heavy
canopy cover), the minimum area-restricted search one should be
able to identify using FPT would have a radius of about 200 m.
However, when small-scale peaks were present in our analyses,
they persisted at scales ,200 m despite simulated positional error.
The dominant scale, based on magnitude of varFPT/area,
corresponded to the scale of seasonal space use. Dechen Quinn
reported mean annual home range area of these deer as 1.9 km2
with variation among seasonal means: 1.1 km2 (spring-summer),
1.6 km2 (fall), and 1.7 km2 (winter) . Deer in our study area
typically occupied an area of the landscape during the spring/
summer and fall seasons and migrated short distances to another
area during winter . When the positional error distributions of
5X and 10X were incorporated, we found both the variation and
mean of the location of peaks in varFPT/area increased. This
relationship suggests that as the cloud of potential locations for
each observed location gets larger (increasing variation), the
peak(s) in FPT analyses may shift to larger values. The shift we
observed was ,50 m, a distance that may have little meaning
when identifying scales of seasonal space use for large species like
Secondary peaks in varFPT/area occurred at larger scales
corresponding to the distance separating seasonal space use or
dispersals. Identification of these scales of movement and the
interpretation of the corresponding landscape scale to which
individuals are responding for these behaviors was robust to not
only the positional error observed among stationary collars, but
also multiples of that error. These peaks occurred at scales very
large relative to the simulated positional error distributions. The
range of peak locations for the greatest error simulations (Fig. 6B,
10X) spanned ,200 m. This range suggests that even in situations
where positional error is much greater than observed among
stationary collars, FPT analyses can accurately identify
organization of movement at large scales.
FPT analyses were robust to habitat-specific error in a
forestagriculture landscape where deer select for habitat representing
the extremes of their positional error distributions and this suggests
that our findings are applicable to other landscapes and species.
Error associated with dichotomous use of open and closed habitat
did not influence the spatial scale of detected peaks. Not only
would we suggest that FPT analyses are expected to be robust in
other forest-agriculture landscapes, but likely in other landscapes
where positional error may be habitat- specific, and certainly in
landscapes where the positional error distributions are similar
across the landscape.
The ability to consistently resolve a peak in varFPT/area is
undoubtedly influenced by the magnitude of the peak and the
landscape scale at which the peak occurs. The strong signal for
most deer in the intermediate range (425 m1,675 m) makes it
very difficult for small changes in position along the path to reduce
variability in time spent along the path. Additionally the
intermediate and large-scale peaks occurred at landscape scales
2 orders of magnitude larger than the positional error of the
collars. This is important because we can assume that FPT
analyses for other GPS collared species that exhibit area-restricted
searches at scales .400 m are robust. We limited our positional
error simulations to 10X that observed by stationary collars. Thus,
if positional error is expected to be very large, .100 m, one may
consider evaluating the impact of error on FPT analyses.
We thank J. Brunner, J. Frair, J. Major, and H. B. Underwood for
constructive criticism on earlier drafts of this manuscript.
Conceived and designed the experiments: DMW ACDQ WFP. Performed
the experiments: DMW ACDQ. Analyzed the data: DMW. Contributed
reagents/materials/analysis tools: WFP. Wrote the paper: DMW ACDQ
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