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2010 SOUTHEASTERN NATURALIST 9(2):303–316
Effectiveness of GPS-based Telemetry to Determine
Temporal Changes in Habitat Use and Home-range Sizes
of Red Wolves
John Chadwick1,*, Bud Fazio2, and Melissa Karlin1
Abstract - Four adult male Canis lupus rufus (Red Wolf) were monitored with GPS
collars in 2006–2008 on the Albemarle peninsula of North Carolina in the first high
temporal resolution (4 locations/day) study of this endangered species in the wild. The
Wolves occupied home ranges during 11–18 month observation periods, and the GPS
data were divided into 30-day subsets to evaluate changes in the spatial characteristics
of the home ranges over time. The subset location data were then combined with landcover
maps derived from Landsat satellite imagery. Proportions of different land-cover
types occupied by the Wolves were seasonally cyclic, with increased use of agricultural
areas when tall row crops were available from summer to autumn and increased use of
adjacent grass, brush, and forest areas from winter to late spring when tall crops were absent.
The spatial extents of home ranges (95% fixed-kernel probability areas) were also
seasonally variable, reaching maximum sizes (73–121 km2) in early autumn to winter
and contracting by 40% to 63% during whelping and pup-rearing in the spring. Our
study shows the potential for GPS collars to provide useful information about space and
habitat use by Red Wolves, and that at least a full year of observation may be required to
fully determine the variability of home-range characteristics.
Canis lupus rufus Audubon and Bachman (Red Wolf) historically ranged
over an extensive portion of the southeastern US, and possibly throughout the
eastern woodlands to Maine (Nowak 1979, 2002; Paradiso and Nowak 1971;
Riley and McBride 1972). Extermination and habitat loss dramatically reduced
the population during the 19th and 20th centuries, and by the 1960s they
only survived in isolated, marginal habitats in Texas and Louisiana (Carley
1975, Shaw 1975). On the verge of extinction, they were federally listed as
endangered in 1967, and the remaining wild Wolves were brought into captivity
between 1973 and 1980 to begin a captive-breeding program (McCarley
and Carley 1979, USFWS 1990, van Manen et al. 2000). The Red Wolf was
thought to be extinct in the wild by 1980 (McCarley and Carley 1979).
Based on morphological criteria, 14 founders were selected for the Red
Wolf captive-breeding program. That program was successful, and 4 pairs
of Wolves were reintroduced in the wild in 1987 on the Albemarle Peninsula
in eastern North Carolina (Parker 1987, Phillips 1994). The reintroduction
and ongoing management program have been successful, with annual counts
1Department of Geography and Earth Sciences, University of North Carolina at Charlotte,
Charlotte, NC 28223; 2US Fish and Wildlife Service, New Mexico Ecological
Services Field Office, Albuquerque, NM 87113. *Corresponding author djchadwi@
304 Southeastern Naturalist Vol. 9, No. 2
between 114 and 131 individuals during 1999–2007, 10 to 22 wild breeding
social groups, and 33 to 55 pups born per year. The reintroduced population
is intensively managed by the USFWS. Although the Wolves are federally
listed as an endangered species (USFWS 2007), this population is considered
a non-essential experimental population (Parker and Phillips 1991).
The US Fish and Wildlife Service (USFWS) Red Wolf recovery and
management area encompassed about 6900 km2 in portions of 5 counties
on the Albemarle Peninsula (Fig. 1). That area included Alligator River and
Pocosin Lakes National Wildlife Refuges and private lands largely consisting
of agriculture (corn, soybeans, and cotton in summer and fall, winter
wheat in winter and spring) and managed pine plantations. Approximately
half of the recovery area consisted of managed and native forests, but these
were fragmented by farms, open areas due to recent timber management, and
stands in various stages of regeneration. Cypress trees and pocosin wetlands
dominated the coastal areas of the peninsula.
An animal’s home range is the spatial expression of its survival and
reproductive behaviors (Burt 1943), and can be influenced by seasonal
environmental variation (Wingfield 2005). Previous studies of Red Wolf
home-range characteristics were limited to durations of a few months using
conventional radiotelemetry collars and abdominal transmitters (Beck
2005, Hinton 2006, Mauney 2005). These studies revealed that Red Wolves
Figure 1. The Red Wolf recovery area located on the Albemarle Peninsula in northeastern
North Carolina, encompassing approximately 6900 km2 in 5 rural counties.
Hachured boxes show the 3 general locations of home ranges of the 4 Wolves in the
study (Wolves 11326M and 11373M shared a similar area). Federal lands, including
Pocosin Lakes and Alligator River National Wildlife Refuges (PLNWR and
ARNWR) are shown in gray.
2010 J. Chadwick, B. Fazio, and M. Karlin 305
are habitat generalists, with highly variable home ranges (7.8–272.8 km2).
In-depth knowledge of Red Wolf home-range characteristics and how they
vary over time can improve current management practices, aid with determining
the carrying capacity of the Albemarle management area for this
species, and enhance the success of future reintroduction projects (USFWS
1990) elsewhere within the historic range of the Red Wolf.
Animal-tracking collars with global positioning system (GPS) capability
can acquire frequent, accurate positions over long periods of time and have
become an important tool for monitoring and managing free-ranging canids
and other species over a broad range of temporal and spatial conditions (e.g.,
Demma et al. 2007). Our primary goal was to test the utility of GPS data to
determine the temporal variability in patterns of land-cover use and the sizes
of home ranges of Red Wolves on the Albemarle Peninsula.
GPS data acquisition and processing
We collected GPS locations from 4 adult male Wolves and mapped landcover
types in their home ranges using multispectral Landsat Thematic Mapper
satellite imagery. All 4 Wolves were wild-born in the recovery area in
2004, and all but Wolf 11333 had been previously captured by USFWS personnel
prior to our study (C. Lucash, USFWS, Columbia, NC, pers. comm.). Two
of the Wolves were collared and released in 2006 and 2 in 2007 (Table 1). The
Wolves were captured as part of routine management activities in which a portion
of the population is captured to radiocollar new individuals, replace aging
or failed collars, manage breeding- and health-related issues, and to respond
to complaints by local residents. Following capture, the animals were transported
in kennels to a central processing facility where biologists conducted
a physical examination, administered vaccines, and collected a blood sample.
After the GPS collars were fitted on the Wolves, the animals were released by
USFWS personnel near their capture location.
We used Lotek model 4400S GPS collars (Newmarket, ON, Canada).
With no differential correction, the manufacturer reported a horizontal error
of <35 m for 95% of acquired locations. After deployment, the collars collected
locations 4 times per day, during evening through morning hours, at
4-hour intervals (2000, 0000, 0400, and 0800 hours; local Standard Time;
Table 1. Observation periods and GPS data summary for 4 Red Wolves, Albemarle Peninsula,
Animal no. Release date Observation period (months) No. locations success rateA
11301M 18-Jan-06 Apr 06–Sep 07 18 1823 83%
11333M 18-Jan-06 Mar 06–Jan 07 11 1271 95%
11326M 31-Mar-07 May 07–Aug 08 16 1774 91%
11373M 31-Mar-07 May 07–Apr 08 12 1340 92%
AProportion of all scheduled GPS locations that were successfully acquired by each collar.
306 Southeastern Naturalist Vol. 9, No. 2
1 hour later during Daylight Savings Time) when the Wolves were thought
to be most active.
Each collar was programmed to emit a VHF locator beacon each day for
4 hours, enabling USFWS field personnel to locate the Wolves every 4–6
weeks and download the stored data. To acquire the data, Wolves typically
were first tracked from an airplane to identify the general location, allowing
ground teams to download the data from a location approximately
100–300 m away, the distance depending on vegetation density or the presence
of other physical barriers.
At the end of the monitoring period, we divided the final data sets for
each Wolf into 30-day increments (i.e., 120 GPS location attempts). Two
locations (of the 4/day) between each 30-day subset were not used, resulting
in a total of 12 subsets of equal duration spanning a calendar year (equaling
365.5 days; the first 2 locations from 1 January were used in both the first and
last subsets of a calendar year). These 30-day periods closely approximate
calendar months for ease of interpretation (e.g., the first 30-day period approximates
the month of January [January 1–30], the second 30-day period
approximates February [January 31–March 2], and so on) and have durations
that are short enough to reveal short-term and seasonal variations in patterns
of land use and home-range sizes.
Satellite image data processing
Preliminary field observations suggested that the Wolves occupied large
(>75 km2), contiguous blocks of agricultural fields at least part of the time.
To investigate variability in the proportions of different land-cover types
occupied by the Wolves over time, we mapped land cover in the study area
using Landsat 5 Thematic Mapper (TM) satellite images (US Geological
Survey, Sioux Falls, SD; 30-m spatial resolution; Path 14, Row 35). The
cloud-free images were acquired on 1 August 2006, 20 August 2007, and 6
August 2008, when crops were near their peak and prior to any agricultural
harvesting or leaf senescence of trees.
To produce land-use maps for each year, we used ENVI image-processing
software (ITT Visual Information Solutions, White Plains, NY) and a
conventional maximum likelihood supervised classifier (e.g., Foody et al.
1992, Swain and Davis 1978), which categorizes each image pixel into
a spectral class based on its brightness values in the 6 visible, near-, and
mid-infrared spectral bands. Ground-truthing was conducted to identify
the 8 dominant summer land-cover types in the study area (corn, soybeans,
cotton, managed conifer forest, native mixed deciduous-conifer forest, bare
soil [recent timber harvest areas], tall wild grasses or brush in previously
harvested forest stands or fallow agriculture fields, and water), to locate
training and validation areas for each class, and to evaluate the accuracy
of the land-cover mapping. We obtained GPS coordinates for 10 locations
within each land-cover type. We used 5 locations for each land-cover type as
training observations for image classification and the remaining 5 to assess
the accuracy of the land-cover classification. We used minimum-mapping2010
J. Chadwick, B. Fazio, and M. Karlin 307
unit (Saura 2002) and pixel-clumping techniques to consolidate and remove
small regions of minor classes. Overall classification accuracy was 90–93%
for the 3 images, and classification accuracy exceeded 75% for all classes.
Commission errors were highest for the 2 forest classes (9–19% commission
of conifer pixels in the mixed trees class) and 2 agriculture classes (23–26%
commission of soybean pixels in the cotton class). Other than annual rotation
of crops, little land-cover change took place (<10 km2 total area) for the
time period corresponding to the 3 images, and most changes were a result of
forest management. In winter months, the areas mapped as corn, soybeans,
and cotton had bare soils or were used to grow winter wheat.
We grouped the classified land-cover types into 3 land-use categories
(excluding water) based on vegetation height and seasonal permanence:
row-crop agriculture, mixed grasses-brush, and forest (managed and native).
We delineated the 3 land-use categories based on the 2006, 2007, and 2008
classified images with vector polygons via heads-up digitizing in ENVI,
and combined the vector maps in a geographic information system (ArcGIS
9.2, ESRI, Inc., Redlands, CA) with the 30-day subsets of GPS data from
the corresponding years to calculate the proportions of the 3 land-use types
associated with Wolf locations during each 30-day interval.
We calculated home-range areas using all GPS data for each Wolf (11–18
months) and for each of the 30-day subsets. We calculated home-range
sizes with a fixed-kernel method, which uses location data to estimate the
probability that an animal will be in a particular location, and delineated
home-range boundaries based on the area encompassing the 95% utilization
distribution (Seaman and Powell 1996, Seaman et al. 1999, Worton 1989).
Kernel methods provide the most accurate measures of space use (Kernohan
et al. 2001, Worton 1995) and exclude large areas not used by an animal
(White and Garrott 1990). We calculated the smoothing parameter (h statistic)
with Animal Space Use 1.2 (Horne and Garton 2007) using likelihood
cross-validation (CVh), which conforms better to the distribution of location
data than least squares cross-validation (Horne and Garton 2006). The value
of the smoothing parameter influences the calculated area of a home range,
so we generated a smoothing parameter for each 30-day subset for each Wolf
and used the average values in the 30-day home-range calculations, which
allowed us to make direct comparisons of the areas over time. This average
smoothing parameter was also used to calculate the long-term home-range
areas using the full GPS datasets.
The overall success rate for scheduled GPS acquisitions by the collars
varied from 83% to 95% (Table 1). For most of the 30-day subsets,
the proportion of successful location acquisitions was relatively high
and consistent (88–100%), except for those from periods 7 and 8
308 Southeastern Naturalist Vol. 9, No. 2
(corresponding with the months of July and August), which were lower
for all 4 Wolves (73–84%).
After release, the Wolves either temporarily occupied a confined area
close to the release site or moved out of the area they would ultimately occupy
as a home range, perhaps because of temporary disorientation, acute
caution, or stress resulting from capture, confinement, and release. For 3 of
the Wolves (11333M, 11326M, and 11373M), we excluded data from our
analyses prior to the second full month after each release to remove the possible
effects of the capture process on their movements, a method similar
to that used in previous GPS studies of canids (e.g., Demma et al. 2007).
Animal 11301M ranged over a wide area beyond its ultimate home-range
boundaries for several weeks, so we did not use data from that Wolf prior to
the third full month.
Wolves 11326M and 11373M were collared and released in late March
of 2007, and began to share the same home range soon after their release.
After May of 2007, 47% of the GPS locations of the 2 males were within
100 m of each other and 64% were within 1000 m. Field records suggest
that 11373M was the sibling of the mate of 11326M, and both animals
were part of an extended family group that included the breeding female
and 3 pups born in April, 2008. Thus, our study involved 4 Wolves in 3
separate areas (Fig. 1).
The 30-day location subsets for each Wolf showed areas with high concentrations
of locations corresponding with the spring denning areas of the
4 Wolves (2007: 11301M and 11333M; 2008: 11326M and 11373M; Fig. 2;
C. Lucash, pers. comm.).
A substantial percentage of all locations for each Wolf were in areas
classified as agriculture (40.0% for 11301M, 66.7% for 11333M, 68.3%
for 11326M, and 63.5% for 11373M). The remaining locations primarily
were in the mixed wild grass-brush areas adjacent (<1 km) to these
agricultural tracts (56.4% for 11301M, 30.6% for 11333M, 29.9% for
11326M, and 33.6% for 11373M). The fewest locations were in the
forest land-use category (3.6% for 11301M, 2.7% for 11333M, 1.8% for
11326M, and 2.9% for 11373M).
The 30-day subsets combined with the land-cover maps showed variability
in the proportions of different land-cover types used by the Wolves
over time. This variability had a seasonal pattern, and the timing was similar
among the 4 Wolves (Table 2, Fig. 3A). In summer and early autumn
(periods 7–10; July–October), all Wolves increased their use of agricultural
fields, with the greatest use occurring in July, August, and September. In
winter to spring (periods 11–12 and 1–5; November–May), the proportion
of locations in agricultural areas were substantially lower, and the use of
grass-brush and forest areas adjacent to agricultural fields increased. Use
of agricultural areas generally was lowest in winter and spring (Table 2,
2010 J. Chadwick, B. Fazio, and M. Karlin 309
Home-range areas for all collected GPS data (11–18 months) for each
Wolf were 118.3 km2 for 11301M, 81.6 km2 for 11333M, 149.5 km2 for
11326M, and 148.1 km2 for 11373M. The 30-day home ranges were highly
variable, but showed similar temporal patterns among the 4 Wolves (Table 2,
Fig. 3B). The 5 largest areas calculated for each Wolf occurred almost exclusively
in late summer to winter (periods 9–12 and 1–3; September–March;
Table 2). The single exception was the large area we observed for 11301M
(80.4 km2) in June 2006. Home ranges for period 1 (January of 2007 and
2008) were at or near their maximum annual size (94–100%; Fig. 3B).
Home-range areas were smallest during spring and summer (periods 4–8;
April to August; Table 2). These home-range areas were 40 to 63% smaller
than the largest seasonal home ranges.
The Wolves in our study showed considerable temporal variation with
regard to land-cover use and home-range size but the temporal patterns were
similar for the 4 animals over the study period (Fig. 3). Although Wolves
Figure 2. Fixed-kernel home-range boundaries (95%) for Wolf 11326M, Albemarle
Peninsula, NC. The 30-day home range reached maximum size (A) in October 2007
(120.5 km2) when agriculture land use was near a maximum (89.3% of locations).
By April 2008 (B), the home range had decreased to 64.1 km2, and agriculture land
use was near a minimum (65.5% of locations). Pups were born to the mate of this
Wolf in late April, and a large proportion of locations during the study period were
in the den area.
310 Southeastern Naturalist Vol. 9, No. 2
Table 2. Proportions of home ranges classified as agricultural land use and home-range areas (km2) of 4 Red Wolves, Albemarle Peninsula, NC, 2006–2008.
30-day Period: 1 (Jan) 2 (Feb) 3 (Mar) 4 (Apr) 5 (May) 6 (June) 7 (July) 8 (Aug) 9 (Sep) 10 (Oct) 11 (Nov) 12 (Dec)
2006 % agriculture - - - 6.1 21.2 37.1 78.3 65.9 70.0 63.8 31.3 29.1
2007 % agriculture 35.1 28.4 33.3 4.2 7.9 46.9 64.1 70.9 81.3 - - -
2006 home-range area - - - 51.7 61.8 80.4 58.0 58.1 74.1 81.6 73.0 78.0
2007 home-range area 96.4 83.4 84.4 48.0 35.3 68.1 58.6 54.0 59.4 - - -
2006 % agriculture - - 61.9 70.8 58.3 79.6 86.2 83.8 85.8 73.6 59.5 50.0
2007 % agriculture 61.2 - - - - - - - - - - -
2006 home-range area - - 63.5 52.4 44.0 50.6 44.5 51.1 70.2 68.0 70.7 73.0
2007 home-range area 69.3 - - - - - - - - - - -
2007 % agriculture - - - - 56.4 79.6 85.3 94.1 88.8 89.3 69.5 62.5
2008 % agriculture 58.1 55.0 56.9 65.5 63.6 83.5 91.5 94.3 - - - -
2007 home-range area - - - - 98.6 94.5 90.6 109.4 114.9 120.5 113.4 113.9
2008 home-range area 116.1 99.3 74.1 64.1 54.3 65.8 72.0 62.6 - - - -
2007 % agriculture - - - - 37.6 75.2 85.6 94.8 85.2 88.3 67.5 66.4
2008 % agriculture 58.5 9.6 30.8 31.3 - - - - - - - -
2007 home-range area - - - - 76.1 61.0 75.3 95.5 107.3 112.5 111.9 120.9
2008 home-range area 113.5 105.3 86.8 50.7 - - - - - - - -
2010 J. Chadwick, B. Fazio, and M. Karlin 311
Figure 3. Time series of the proportions of GPS locations in agricultural land-use
areas (A) and home-range areas (B) for the 4 Red Wolves in this study, Albemarle
Peninsula, NC, 2006–2008. Graphs start in period 5 (May). (A) Monthly proportions
of locations within mapped agricultural regions were seasonally cyclic and
similar among the study Wolves, with maxima reached in summer months when
row crops were tallest (gray area). (B) Monthly home-range areas, shown as the
percentage of the maximum observed area for each Wolf. Home ranges generally
were smaller in the spring, during whelping and pup-rearing periods (gray area),
followed by a gradual increase in size during summer and autumn, and were largest
in late autumn and winter. For all 4 Wolves, home ranges were 94–100% of
their observed maxima in January.
312 Southeastern Naturalist Vol. 9, No. 2
11326M and 11373M were in the same family group, their temporal patterns
were similar to those of the other 2 Wolves, which occurred in different family
groups and occupied separate home-range areas. Thus, our data may be
suggestive of population-wide spatio-temporal patterns of Red Wolf movements
and land use.
The observed shift from use of row-crop agriculture fields to other cover
types in November coincided with intense agricultural harvesting of tall
(>1 m) row crops. Corn harvesting began in September of each year, but
agricultural cover was not completely removed until soybean and cotton
harvesting was completed in November. Hunting seasons for various game
animals also began in November, and the combination of the removal of tall
agricultural cover and human disturbance may have forced the Wolves to
increase their occupation of areas with year-round vegetation, which often
occurred on the periphery of agricultural fields. The growth and harvest
cycles of agricultural vegetation may also influence the primary prey species
of Red Wolves, such as Odocoileus virginianus Boddaert (White-tailed
Deer), which also may have contributed to the seasonal shifts in habitat use
we observed. Additional studies will be required to fully characterize the
temporal patterns of habitat use we observed, including those of Wolves that
do not occupy agricultural fields.
Animal home ranges can be influenced by many factors (e.g., human
activities, prey availability, conflicts with other animals, injuries; Wingfield
2005), but a seasonal pattern was apparent in our study. We observed a gradual
increase in the extent of home ranges from May–June to January, when
all home ranges were at or near their maximum annual size (Fig. 3B). The
decline in home-range size after January coincided with mating, den preparation,
and whelping during February–April, and the presence of relatively
immobile pups and frequent visits to den sites by adults until early summer
(C. Lucash, pers. comm.). Home ranges then gradually increased in size until
autumn, when pups start fully participating in hunting activities with adults.
This interpretation is consistent with studies of other canids. Canis lupus
Richardson (Gray Wolf) exhibit lower mobility and more restricted activity
near den sites during denning periods (Ballard et al. 1991, Harrington and
Mech 1982, Walton et al. 2001). Home ranges of Canis latrans Say (Coyote)
also vary seasonally (Laundré and Keller 1984).
The ≈10% reduction in successful GPS location acquisitions during July
and August likely resulted in an underestimate of home-range size and use
of agricultural areas. Previous studies have shown that thick summer vegetation
can block GPS signals, resulting in fewer successful satellite fixes
(Dussault et al. 2001, Rempel et al. 1995). The Wolves may have spent more
time during these warmer months in a supine position in dense vegetation,
which would have decreased the number of acquired locations.
The results of our pilot study indicated that home ranges of the 4
Wolves were not static over the course of a year and thus could not be fully
2010 J. Chadwick, B. Fazio, and M. Karlin 313
characterized by short-term (days or weeks to a few months) observations.
We suggest that future studies of the spatial behavior and habitat use
of Red Wolves should be at least 1 year in duration. In-depth knowledge
of Red Wolf spatial characteristics can be used to improve current
management practices in the Albemarle reintroduction area and enhance
the success of future reintroduction projects elsewhere within their historic
range. For example, Coyote introgression and hybridization with Red
Wolves has become one of the most urgent management problems in the
recovery area, as the numbers of Coyotes continue to increase in eastern
North America (Kelly et al. 1999, Phillips et al. 2003) and are increasingly
observed and trapped within the Red Wolf management area (C. Lucash,
pers. comm.). If the contraction of home ranges observed during the spring
is common among Red Wolves with established home ranges, this could
result in a temporary increase of the extent of unoccupied areas between
home ranges. We speculate these temporarily unoccupied areas may be
exploited by Coyotes during spring and summer months to enter the management
area, possibly exacerbating this significant management problem.
Future studies should acquire simultaneous location data from both Wolves
and Coyotes in the Red Wolf management area to test this hypothesis and
to better understand how the two species interact.
GPS locations (4 location attempts/day) from 4 Red Wolves acquired
during 2006–2008 showed that land use and home-range sizes were highly
variable over time. Long-term (>1 yr) location data would be necessary to
fully characterize the variability and complexity of space use and movements
of the species. Based on land-use data from satellite imagery, all 4
Wolves increased their use of agricultural areas when tall (>1 m) crops were
present from summer to early autumn, and increased their use of natural, tall
grass-brush areas when tall crops were not present (late autumn to spring).
Home-range areas based on short-term data (30 days) also varied seasonally,
and the temporal patterns were similar among the 4 Wolves. Home ranges
were largest during late summer to winter, followed by a 40 to 63% reduction
in size during the mating and whelping periods in spring, with a gradual
increase in size over the summer.
We thank C. Lucash and F. Mauney of the US Fish and Wildlife Service for their
assistance with Wolf captures, collar placement, data collection, and field work. We
also thank R. McGee and Z. Petersen for assistance in the field and with image and
GPS data processing. We also thank editor Frank van Manen and two anonymous
reviewers for their valuable assistance with this manuscript. This project was funded
by a NASA/North Carolina Space Grant and by the University of North Carolina
at Charlotte. The findings and conclusions in this article are those of the authors
and do not necessarily represent the views of the US Fish and Wildlife Service.
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