Original Article
Land Parcelization and Deer Population
Densities in a Rural County of Virginia
KAREN R. LOVELY, National Zoological Park, Smithsonian Conservation Biology Institute, 1500 Remount Road, Front Royal, VA 22630, USA
WILLIAM J. McSHEA,1 National Zoological Park, Smithsonian Conservation Biology Institute, 1500 Remount Road, Front Royal, VA 22630, USA
NELSON W. LAFON, Virginia Department of Game and Inland Fisheries, 1132 Thomas Jefferson Road, Forest, VA 24551, USA
DAVID E. CARR, University of Virginia, Blandy Experimental Farm, 400 Blandy Farm Lane, Boyce, VA 22620, USA
ABSTRACT The parcelization of exurban landscapes creates a matrix of intermediately sized and privately
managed land parcels, presenting a unique challenge to wildlife managers. During 2010?2011, we studied the
correlates between land parcelization, deer density, and hunting patterns in exurban northwestern Virginia,
USA. We estimated October deer densities (no. deer/km2) and conducted landowner surveys of deer harvest
in 13 study blocks of mean size 34.8 km2. The extent of parcelization varied between study blocks; mean
parcel size ranged from 2.00 ha to 26.12 ha. We used distance-sampling techniques to survey pre-harvest
deer densities in each section, with estimated densities ranging from 9.4 deer/km2 to 30.1 deer/km2. We
quanti?ed deer harvest through calculations of harvest density and the percentage of land hunted. As parcel
size increased, the percentage of land hunted increased. Harvest densities reported by landowners, however,
remained constant with the exception of 2.0?4.0-ha parcels, which had higher harvest densities than
60.8?161.8-ha parcels. We used linear regression analysis to model the response of deer density (natural
log) to landscape metrics, and the best-?t model predicted deer density from mean parcel size with
equivalent models including habitat with mean parcel size. Our results suggest that development
processes that subdivide rural lands can signi?cantly increase deer populations. The mechanism for this
increase may be restricted hunter access to smaller property parcels and-or increased probability of deer
refuges nearby. 
 2013 The Wildlife Society.
KEY WORDS density estimation, human?wildlife conflict, modeling,Odocoileus virginianus, parcelization, population
management, Virginia, white-tailed deer.
In recent decades, management of white-tailed deer
(Odocoileus virginianus) populations in Virginia, USA, and
throughout the eastern United States has shifted from pop-
ulation restoration to stabilization and reduction (Foster
et al. 1997, Virginia Department of Game and Inland
Fisheries 2007). High-density deer populations in?ict sig-
ni?cant economic damage to agricultural crops, landscaped
gardens, and through vehicle collisions (Conover 1984,West
and Parkhurst 2002, VerCauteren et al. 2006, McShea et al.
2008) in addition to causing ecological damage to forest
species composition and regeneration (Alverson et al. 1988,
Stromayer and Warren 1997, Warren 2011, McShea 2012).
Forage limits, predation, hunter harvest, and severe winter
conditions are the principal mechanisms that reduce ungu-
late population size (Patterson and Power 2002, Robinson
et al. 2002), with regulated hunting being the primary meth-
od used by wildlife managers to keep deer density at ??cultural
carrying capacity???the number of deer that can compatibly
coexist with human populations (Knox 1997, Brown et al.
2000). Deer overabundance can be de?ned in ecological and
economic terms (Warren 2011). In areas of swelling subur-
ban and exurban development, deer density is a concern due
to the prevalence of deer?vehicle collisions and deer-in?icted
landscape damage (DeNicola et al. 2000, Storm et al. 2007a,
McShea et al. 2008). The transition from rural to exurban or
suburban development is coupled by increased parcelization
(i.e., the division of large land blocks under single ownership
into small blocks under multiple owners), and the resulting
fragmented landownership adds further complexity to wild-
life management (Harden et al. 2005).
In designing wildlife management solutions, the impacts
of exurban development and land parcelization on deer
population structure and hunting ef?cacy must be considered
(Vogel 1989, Foster et al. 1997, Lopez et al. 2004, Harden
et al. 2005). Forest fragmentation intensi?es with human
development, producing a patchwork of different vegetation
types across a landscape (Saunders et al. 1991, Theobald et al.
1997, Brooks 2003). Initial fragmentation alters the ratio
between cover and forage habitat, creating additional edge
and increasing the carrying capacity for deer populations
(Alverson et al. 1988, Roseberry and Woolf 1998). Studies
in both Montana (Vogel 1989) and Florida (Lopez et al.
2004), USA, indicate that the highest deer densities are
found at intermediate housing levels where there is increased
edge habitat and decreased hunting pressure (Foster et al.
1997, Harden et al. 2005). Land ownership patterns may
Received: 7 February 2012; Accepted: 24 September 2012
1E-mail: mcsheaw@si.edu
Wildlife Society Bulletin; DOI: 10.1002/wsb.244
Lovely et al. 
 Land Parcelization and Deer Densities 1
also impact deer populations by indirectly restricting hunter
ability to traverse property boundaries.
Our objective was to examine the correlates between par-
celization of rural properties, deer population size, and hunt-
ing patterns at a local scale. We expected that increased
subdivision of land ownership would be associated with
increased deer density and decreased harvest density.
STUDY AREA
We selected 11 similar-sized study blocks in Frederick
County, a rural northwestern Virginia county targeted for
enhanced deer control due to a positive outbreak of chronic
wasting disease (CWD). This study was part of a larger effort
to monitor deer population density in a region of Virginia
where wildlife management focused on reducing the deer
population and combating the spread of CWD. We also
selected 2 study blocks in a nearby county (Rappahannock
County) with similar habitat characteristics and housing
densities. Major roads and county borders delineated the
13 study blocks of mean size 34.8 km2 that had intermediate
levels of forest cover (59?91%), housing density (3.5?
18.8 houses/km2), and road density (0.7?2.23 km/km2)
and were indicative of the overall forest, house, and road
presence in western Frederick County. The dominant land-
cover types were forest and ?eld, comprising an average of
72.5% and 18.6%, respectively, of the total land cover in each
study block. The mean July high temperature in the region
was 30.08 C, the mean January low temperature was
3.38 C, and the average annual precipitation was moderate
(108.5 cm; Hayden and Michaels 2000). The majority of
land was privately owned (>96%), with one federal property
(George Washington National Forest) and no state-owned
lands.
METHODS
Hunting Data
We identi?ed all landowners in each study block using the
county tax map records, and willing landowners completed a
brief survey between September 2010 and February 2011.
The Smithsonian Institutional Review Board for Human
Subjects Research approved the survey methods (human
subjects protocol no. HS11020). The survey comprised 10
questions. Three questions regarded 2009 deer harvest on
family-owned property (Did you or anyone else hunt deer on
this property in 2009? If yes, how many deer were removed
from this property in 2009? How many deer were taken with
bow? Muzzleloader? Ri?e?). The remaining questions in-
quired about landowner knowledge of CWD. Landowners
were approached directly (i.e., door-to-door, deer check
stations, voting polls, public forums), as well as through
telephone calls and direct mailings. Landowners were given
until February 2011 to return mailed surveys. We surveyed
property renters or farm managers when they were more
familiar than the landowner with hunting patterns. Survey
teams did not concentrate on the deer hunting community,
because 98% of interviews were obtained through efforts that
did not single out hunters (voting polls, door-to-door, mail-
ings, and telephone calls).
All adjacent property parcels under the same ownership
were merged and treated as a single, larger parcel.
Landowners with non-adjacent parcels provided indepen-
dent data for each property. If a landowner did not have
harvest information on each non-adjacent parcel, we allocat-
ed harvest to each parcel based on its percent of the total area
(<1% of parcels).
In each study block, we split surveyed properties between 7
groups de?ned by parcel size (in ha): <2.0 (5 acres), 2.0?4.0
(5?10 acres), 4.1?8.0 (10?20 acres), 8.1?20.2 (20?50 acres),
20.3?60.7 (50?150 acres), 60.8?161.8 (150?400 acres), and
>161.8 ha. The group cut-offs were based on acres because
county and state administrators, when handling land-use
matters, use that metric. The range of parcel sizes within
each category was determined from visual examination of the
data, and we strove to create a relatively equal number of
properties in each category. Survey data were pooled by study
block and parcel size. Within all 13 study blocks, we calcu-
lated the percentage of land hunted and harvest density (i.e.,
average no. deer harvested/km2) on all the surveyed land in
each size class. We also calculated the percentage of land
hunted and harvest density for the surveyed land in the
individual study blocks. We ran an analysis of variance
with a Bonferroni correction to determine the relationships
between parcel size, harvest density, and the percentage of
land hunted. All statistical analysis was performed using R
for Mac OS X (The R Foundation, Vienna, Austria; http://
www.R-project.org) with a signi?cance level de?ned by
P < 0.05.
We used ArcGIS 9.3 to map the occurrence of hunting
across surveyed land parcels. County of?cials in both
Frederick and Rappahannock counties provided digitized
maps of all property parcels in the study sections. Land
parcels were coded as hunted, not hunted, or no data. We
laid a grid of 0.25-km2 cells over each study block, deter-
mined the total number of grid cells comprising each section,
and counted the number of grid cells containing land that
was not hunted. Within each study block, we calculated the
likelihood of a given grid cell containing land that was not
hunted.We ran a linear regression to explore the relationship
between the mean parcel size of each study block and the
likelihood of cells containing land with no hunting (i.e., a
deer refuge). We considered this measure to be an index of
refuge distribution because we did not have deer harvest data
for all parcels within each study block despite our extensive
survey coverage (approx. 50% of each study block).
Deer Population Density
We estimated deer densities in the 13 study blocks through
spotlighting protocols, a common method used to measure
deer population size (McShea et al. 2008, 2011; Hubbard
and Nielsen 2011). Density counts were adjusted using
distance-sampling techniques to correct for the majority of
error associated with spotlighting (Buckland et al. 2001,
Focardi et al. 2001, Collier et al. 2007). Spotlighting from
public roads onto private property is only legal in Virginia
2 Wildlife Society Bulletin
with landowner permission, which we obtained during the
survey effort. State and local law enforcement of?cials were
noti?ed prior to spotlighting.
Prior to ?rearm hunting season, we surveyed for 8 nights
between 19 October and 28 October 2010 from 2030 hours
until 0200 hours. We did not conduct surveys on nights with
steady rain, excessive wind (>6.6 m/s) or extensive fog.
Surveys were conducted from pick-up trucks, each manned
by a driver, a data recorder, and 2 observers who shone
3-million candlepower Brinkmann (The Brinkmann
Corporation, Dallas, TX) spotlights from the truck?s bed
while traveling at <2.2 m/s on predetermined public roads.
All public roads within a study block were surveyed except
those with >2 lanes and speed limits >80 km/hour.
Observers simultaneously rotated one spotlight between 08
(directly in front of the truck) and 908, while the other was
rotated between 08 and 2708. Once a deer was sighted, we
stopped the truck and determined the group size, distance,
and sighting angle to the location where the deer was initially
spotted. We measured distances with laser range?nders and
determined angles with handheld compasses. We recorded a
group of deer (i.e., deer at rest within 6 m of one another,
deer grazing within 6 m of one another, or deer in motion
traveling the same direction and within 6 m of one another
[LaGory 1986]) as a single sighting. We drove stretches of
public road within a single study block that could be contin-
uously spotlighted (i.e., transects) only once each night.
Transects ranged in length from 2 km to 15 km. We sur-
veyed transects 2?4 times until we recorded >40 deer sight-
ings in each study block. Thomas et al. (2010) recommend
using a sample size of >60 sightings; however, the logistical
problems imposed by spotlighting across a matrix of private
property forced us to accept a smaller number of sightings.
Land use along transects was a matrix of open and forested
land. When transects were pooled by study section, the
percentage of open land bordering transects ranged from
23% to 81%. Although there was variability in land cover,
we surveyed after leaf fall to minimize the differences be-
tween sighting distances in different habitat types. Road
density also varied between survey blocks, potentially affect-
ing the distribution of deer across the landscape and the
proportion of land spotlighted in each study block (Cassey
and McArdle 1999). To compensate for road bias, we sur-
veyed later in the evening when traf?c was light.
The program DISTANCE 6.0 (The Distance Sampling
Team, St. Andrews, Scotland, http://www.ruwpa.st-and.
ac.uk/distance/) was used to estimate density for each block.
We right-truncated the data as necessary and used the
Akaike Information Criterion (AIC) to determine the
best-?t model (Akaike 1973, Burnham and Anderson
2002). Buckland et al. (2001) recommended truncation to
remove outliers from the analysis. We did not use left-
truncation to compensate for road bias. Instead, we expanded
the distance interval closest to the transect to include deer
that may have shifted due to the presence of the vehicle
(McShea et al. 2011). We altered model parameters, such as
key function and series expansion, in order to model 2?3
different detection functions/study block and obtain an
estimate with con?dence intervals of <0.25 (Thomas
et al. 2010).
Impacts of Parcelization and Habitat on Density
We used ArcGIS 9.3 to determine a suite of landscape
variables characterizing the 13 survey blocks. The digitized
maps provided by Frederick County and Rappahannock
County of?cials included a layer with roads and a layer
mapping residential buildings. We calculated housing den-
sity by dividing the number of residential buildings in each
study block by the section?s area. Similarly, we calculated
road density by dividing the total length of road (public and
private) by the area of each study block. We reclassi?ed the
land-cover types in the 2005 Virginia Land Use Geographic
Information System layer (Virginia Department of Forestry
2005) into 6 categories: forest (hardwood, pine, mixed,
harvest), ?eld (crop, grassland), development (pavement,
residential, industrial), grazed pasture, apple orchard, and
other. Land-cover resolution was 15  15 m2. The land-
cover data de?nes forest as an undeveloped area with trees
occupying >75% of the cover (Virginia Department of
Forestry 2005). The Department of Forestry data did not
distinguish grazed pasture and apple orchard from cropland,
so all plots of cropland along public roads within the study
blocks were visually inspected and placed into the appropri-
ate category. We summed the number of pixels within each
study block to determine the percent cover of each land type.
Three parameters of human development (mean parcel
size, housing density, and road density) and 3 land-cover
variables (percent forest cover, ?eld cover, and orchard pres-
ence?absence) characterized each study block. The mean and
median parcel sizes of each survey block were highly corre-
lated (r ? 0.87), and there was no statistical difference when
each metric was used as the indicator of parcel size. Due to a
signi?cant correlation between mean parcel size, housing
density, and road density (jRj > 0.840), we selected the
single variable, mean parcel size, to represent these param-
eters in our model. Parcel size is a straightforward measure
and easily estimated at county planning of?ces.
We used simple and multiple linear-regression analysis to
develop a model explaining the relationship between deer
density (natural log) and 4 independent landscape metrics:
mean parcel size, percent forest cover, percent ?eld cover, and
orchard presence?absence. We selected these parameters
because they make biological sense, as prior research indi-
cates that human development and land cover impact deer
density (Vogel 1989, Roseberry and Woolf 1998, Harden
et al. 2005, Storm et al. 2007b). We ranked models using
the AIC and considered all models with DAIC < 2 as
competitive (Akaike 1973). Model analysis presented us
with 7 competing models.
RESULTS
Hunting Data
Surveys were completed by 1,343 landowners, providing
2009 deer harvest data for 30?70% of all the land in
each study block. Seventy-two percent of all the landowners
contacted completed surveys, and 95% of the uncompleted
Lovely et al. 
 Land Parcelization and Deer Densities 3
surveys were from the direct mailings. Properties of all size
classes were surveyed (Table 1) and 78% of survey land-
owners allowed us to use spotlights to survey for deer.
Surveying methods varied in success; door-to-door efforts
resulted in the most completed surveys (45%), followed by
the voting polls (21%) and direct mailings (18%). A consid-
erable portion (60?95%) of the surveyed land in each study
block was harvested for deer, while harvest density, as iden-
ti?ed by landowners who harvested 0?100 deer from indi-
vidual property parcels, ranged from 5.8 deer to 20.5
deer harvested/km2 (Table 2).
Parcel Size and Deer Harvest
Parcel size in each study block followed a unimodal proba-
bility distribution with means ranging from 2.00 ha to
26.12 ha (x ? 11.96 ha; Table 3). When all 13 study blocks
were considered, we had deer harvest data (2009) for 2,311
property parcels.
The percentage of land hunted differed between property
size classes. On average, when properties <2.0 ha were
pooled, 16% of the total land area was hunted and the percent
steadily increased with hunting occurring on 100% of the
land comprising parcels >161.8 ha (F6,81 ? 39.947,
P < 0.001; Fig. 1). There were differences between the
property size classes, as the likelihood of hunting increased
with parcel size (Fig. 1).
Harvest density did not increase with parcel size; rather, it
was greatest for the 2.0?4.0-ha size class (x ? 23 deer/km2)
and declined as property size increased (F6,83 ? 2.486,
P ? 0.029; Fig. 1). Harvest density was comparable between
most size classes, but 2.0?4.0-ha parcels had higher harvest
densities than did 60.8?161.8-ha properties (P ? 0.023).
When the data from all the study blocks were pooled, the
mean harvest density was 10.17 deer/km2 and 7.14 deer/km2
for Frederick and Rappahannock counties, respectively.
Across the survey blocks, the likelihood of a 0.25-km2 grid
cell containing land that prohibited hunting ranged from
0.124 to 0.534 (Table 2). The relationship between mean
parcel size and the likelihood of a cell containing land that
was not hunted was evident; for every 1-ha increase in mean
parcel size, the likelihood of a grid cell containing land that
was not hunted for deer declined by 0.010 (P ? 0.009;
R2 ? 0.425).
Parcelization Influences Deer Density
Deer densities in the survey blocks during the pre-ri?e season
(Oct) ranged from 9.4 deer/km2 to 30.1 deer/km2 (Table 2).
Seven models ?tting mean parcel size, forest cover, ?eld
cover, and orchard presence?absence with deer density (nat-
ural log) had comparable ?ts (Table 4). Based on previous
studies (Lopez et al. 2004, Storm et al. 2007b, Gorham and
Porter 2011), it is not surprising that habitat was a factor
in?uencing deer density in 6 of the 7 competing models.
What is surprising, is that mean parcel size (M) contributed
to the top model through the linear function y ?
2.994  0.0188  M (P ? 0.208, R211 ? 0.062) and was
a contributor to all the competing models where it was
combined with habitat measures. Although we examined a
linear ?t for straight-forward comparisons between models,
Table 1. Property parcels, pooled into 7 size classes, in the 13 study blocks of
mean size 34.8 km2 in northern Virginia, USA, 2009?2011. Study blocks
were used to examine correlates between land parcelization, deer density, and
hunting patterns.
Size class (ha)
Land parcels
No. surveyed Total no. parcels
<2.0 1,437a 2,933
2.0?4.0 186 1210
4.1?8.0 223 556
8.1?20.2 211 460
20.3?60.7 158 334
60.8?161.8 76 125
>161.8 20 25
a Ninety-three percent of the parcels are in one study block (Fred.8).
Table 2. Deer harvest and density in 13 study blocks in northern Virginia, USA, 2009?2011. Study blocks were used to examine correlates between land
parcelization, deer density, and hunting patterns.
Study block Area harvesteda (km2) Harvest densityb Refuge likelihoodc
2010 deer density
No. sightings No./km2 LCL?UCLd CVe
Fred.1 19.0 (85.7) 7.5 0.286 42 11.99 8.71?16.51 0.162
Fred.2 5.9 (90.7) 20.5 0.185 41 30.10 19.39?46.72 0.223
Fred.3 10.7 (65.4) 8.5 0.444 44 11.70 7.54?18.16 0.191
Fred.4 30.5 (95.4) 10.1 0.124 41 13.49 8.55?21.29 0.231
Fred.5 13.5 (81.0) 6.1 0.352 45 9.40 6.57?13.46 0.180
Fred.6 12.1 (81.4) 17.0 0.373 43 12.99 7.89?21.39 0.221
Fred.7 9.4 (64.7) 7.6 0.476 44 18.96 13.16?27.31 0.182
Fred.8 10.1 (57.0) 11.2 0.534 47 18.63 13.78?25.18 0.152
Fred.9 17.9 (87.1) 8.8 0.337 49 26.81 16.40?43.84 0.249
Fred.10 19.6 (89.2) 11.3 0.324 53 27.62 18.77?40.64 0.196
Fred.11 7.4 (76.1) 9.0 0.377 42 14.44 9.60?21.72 0.203
Rapp.West 26.1 (76.8) 5.8 0.319 62 13.36 7.73?23.07 0.174
Rapp.East 22.0 (83.7) 8.9 0.299 44 12.17 9.17?16.14 0.142
a No. in parentheses is the portion of land harvested of all surveyed land in the study block.
b Harvest density is the no. of deer harvested/km2.
c Refuge likelihood is the likelihood of a 0.25-km2 grid cell containing land with no hunting.
d LCL and UCL refer to the lower and upper 95% con?dence intervals.
e CV is the coeff. of variation.
4 Wildlife Society Bulletin
the relationship between deer density and parcel size may
also be explained using a curvilinear function that includes
non-linear terms such as y ? 6.519  M  e(0.118  M)
(Fig. 2). The regression coef?cients are different from 0
(P ? 0.005 and P < 0.001, respectively), which suggests
that the non-linear coef?cient contributes to the ?t of the
model.
DISCUSSION
Parcel Size, Harvest Density, and Deer Density
The impact of subdivided property parcels on hunting prac-
tices and deer densities was evident, because smaller parcel
size correlated with increased housing density, increased road
density, and a reduced percentage of land hunted. We found
that decreased parcel size resulted in increased deer densities,
supporting the conclusions of Kretser et al. (2008) that
exurban development creates prime habitat for human-
adapted species such as deer. These results further support
the conclusion of Vogel (1989), who found that deer popu-
lation densities increased as housing density shifted from low
to intermediate. Development in exurbia creates prime deer
habitat; although tree cover is limited in areas of high
development, exurbia creates a landscape that couples forest
cover with abundant food resources (Storm et al. 2007b).
This intermixing of cover and feeding habitat maximizes
deer maneuverability through a landscape (Gorham and
Porter 2011).
The relationship between parcel size and harvest density
was weak. Although less land was hunted for deer when
parcel size was reduced, increased harvest ef?ciency may
have helped maintain harvest density. Harvest density can
be high in smaller parcels due to a more fragmented land-
scape and increased deer vulnerability (Foster et al. 1997).
Furthermore, property owners selectively permit hunting on
private property, a trait that tends to limit the number of
hunters and the total deer harvest on large properties.
The model explaining deer density (natural log) in relation
to mean parcel size was not signi?cant. A curvilinear model
may bemore appropriate, because our results indicate that for
the smallest parcels, where housing and road densities are
Table 3. Attributes of 13 study blocks of land inNorthern Virginia, USA, 2009?2011. Study blocks were used to examine correlates between land parcelization,
deer density, and hunting patterns.
Study block
Land parcels
Forest cover (%)
Road density
(km/km2)
Housing density
(no. houses/km2)
x size (ha) n SD
Fred.1 13.38 284 46.9 76.0 1.34 6.05
Fred.2 9.45 236 21.9 63.7 1.40 8.89
Fred.3 4.98 718 18.3 65.1 1.77 18.80
Fred.4 26.12 182 135.1 91.6 0.64 3.00
Fred.5 11.14 246 19.9 79.1 1.46 6.97
Fred.6 11.20 292 20.1 82.5 1.38 6.15
Fred.7 7.68 420 20.7 61.1 1.45 12.27
Fred.8 2.00 1657 11.1 73.0 2.23 17.17
Fred.9 5.82 636 19.0 78.3 1.49 15.81
Fred.10 10.03 321 21.8 75.1 1.36 9.47
Fred.11 8.65 203 18.4 74.5 1.47 10.46
Rapp.W 24.12 235 61.7 59.9 0.77 3.96
Rapp.E 20.87 213 61.3 63.6 0.87 3.52
Figure 1. Properties pooled by parcel size within each northern Virginia,
USA, study block, 2009?2011. One represents the smallest parcels, and 7
represents the largest parcels. The figures show both the mean percentage of
land hunted for deer within each of the 7 size classes (left figure) and the
mean harvest density (no. deer harvested/km2; right figure). The error bars
represent the upper and lower confidence intervals. Means with different
letters are different (P < 0.05).
Table 4. Candidate linear models with DAIC (Akaike Information
Criterion) <2 that predict deer density (natural log) in 13 study blocks in
northern Virginia, USA, 2009?2011.
Variable DAIC Ka
Mean parcel size 0.00 1
Forest cover 1.70 1
Mean parcel size and orchard presence?absence 1.74 2
Mean parcel size and forest cover 1.86 2
Mean parcel size and field cover 1.91 2
Field Cover 1.92 1
Orchard presence?absence 1.96 1
a K indicates the no. of predictor variables in the model.
Lovely et al. 
 Land Parcelization and Deer Densities 5
highest, the relationship between mean parcel size and deer
density shifts. There appears to be a threshold: deer density
declines when mean parcel size drops below 7 ha. Prior
studies have found a similar shift in deer densities at high
levels of development. In Carbondale, Illinois, USA,
Anderson et al. (2011) found that deer land use was high
in areas with well-spaced human dwellings, but once human
development became clumped, deer presence declined.
Similarly, studies in Montana (Vogel 1989) and Florida
(Lopez et al. 2004) found that deer presence was highest
at intermediate levels of human development. Particularly
during the non-winter months, deer avoid landscapes in close
proximity to human dwellings (Storm et al. 2007b), where
there is less connectivity between forest patches and a re-
duced quality of habitat (Gorham and Porter 2011). This
tipping point weakens our linear model and may lead to an
overestimate in deer density when parcelization is extensive.
We believe the general increase in deer density as mean
parcel size declined was due to a patchwork in landowner
decisions regarding deer management. In developing
rural landscapes, increased parcelization has coincided
with decreased hunter access to land (Harden et al. 2005,
Jagnow et al. 2008, Campa et al. 2011), limiting the ef?cacy
of population reduction and deer management efforts
(McCullough 1984, Brown et al. 2000, Storm et al.
2007a, Bowman 2011). Individual parcels and subdivisions
with ownership restrictions preventing deer harvest effec-
tively create deer refuges. Deer refuges include properties
where hunting is forbidden, as well as virtual refuges where
individual hunters cannot move across the landscape to
access other properties. Our study indicates that as mean
parcel size declined, the likelihood of refuge presence within
a 0.25-km2 grid cell signi?cantly increased. Previous research
in an exurban community of Illinois found that a prevalence
of properties forbidding hunting resulted in a high annual
deer survival rate because the entire home range of a deer
existed on private property where there was little threat of
harvest (Storm et al. 2007a). The consequences of subdivid-
ing deer habitat are less land open for deer harvest, hunting
blocks becoming more isolated, and deer density increasing
(Jagnow et al. 2008, Campa et al. 2011).
Landowner and Spotlighting Surveys
Our survey results indicate that deer harvest ranges from 32%
to 131% of the pre-hunt population estimates. Reported
harvest densities in our study were signi?cantly higher
than those derived from harvest data provided by the
Virginia Department of Game and Inland Fisheries for
the 2009?2010 hunting season. Overall deer harvests
reported by landowners, managers, and tenants may be
over-estimates (Rupp et al. 2000). For the purpose of this
study, relative harvest density is more important than precise
harvest density and should be comparable across study
sections.
Techniques to determine deer harvest, whether through
check stations, surveys, mailings, telephone calls, or report
cards, have long been debated by wildlife managers
(MacDonald and Dillman 1968, Rupp et al. 2000). The
potential for in?ated hunter success and non-response bias
remain concerns during survey efforts (Rupp et al. 2000).
Seventy-six percent of our surveys were from door-to-door
efforts, telephone calls, and the voting polls where >95% of
surveys were completed and where we did not target the
hunting population. Although non-response bias is less of a
concern from these efforts, it may have arisen in the mailed
surveys. Mailed surveys accounted for <20% of the total
surveys, and we have no reason to believe that resulting biases
in harvest reports varied between study sections.
Our study further demonstrates that spotlighting is a fea-
sible method for surveying deer density in a landscape of
private property. There is a potential bias in the density
estimates because the spotlighting surveys were limited to
public roads, violating the assumption of DISTANCE that
the survey line traverses an area with a random distribution of
habitats and land uses (Buckland et al. 2001). Wildlife
managers face such limitations when spotlighting in a matrix
of private property where surveys are restricted to public
roads. Changes in regulations to allow federal and state
employees to spotlight from public roads would simplify
the process of density estimation, but with the support of
law enforcement of?cers, landowner permissions along sur-
vey routes can be secured.
MANAGEMENT IMPLICATIONS
As human populations grow, developments expand, and land
becomes increasingly parcelized, deer?human con?icts will
challenge wildlife managers in suburban and exurban regions
(Harden et al. 2005, Anderson et al. 2011). Concerns re-
garding CWD and disease spread further emphasize the
importance of understanding the variables that impact
deer population size (Williams et al. 2002). To effectively
reduce herd size through regulated hunting, wildlife man-
agers must consider the factors that affect deer density at
small scales. Parcelization restricts hunter access to land
Figure 2. Relationship between mean parcel size (ha) and deer density
(no. deer/km2) in 13 Virginia, USA, study blocks, 2009?2011.
6 Wildlife Society Bulletin
(Harden et al. 2005, Jagnow et al. 2008, Campa et al. 2011);
thus, managers aiming to control deer densities should focus
their activities on gaining hunter access to smaller parcels,
particularly parcels<8 ha. The highest deer densities in rural
Virginia, and therefore the greatest potential for human?deer
con?icts and disease spread, occurred when parcel size
ranged from 5.0 ha to 10.0 ha. Planning agencies should be
made aware of the consequences of subdividing land on the
ability of wildlife managers to regulate deer.
ACKNOWLEDGMENTS
This project was funded under a U.S. Department of
Agriculture cooperative agreement with the Virginia
Department of Game and Inland Fisheries. Virginia
Department of Game and Inland Fisheries and the
University of Virginia provided ?nancial and logistical sup-
port for this study. We thank M. Williamson, M. Kline, and
D. Kozel for their help with data collection; F. Frenzel, M.
Knox, D. Kocka, T. Urgo, H. Tomlinson, C. Grady, R.
Hanger,M.Dye and all the others at Virginia Department of
Game and Inland Fisheries who provided support; J.
McCarthy and C. Wroth from the Rappahannock County
administrative of?ces; A. Cahill from the Frederick County
administrative of?ce; the interns at the Smithsonian
Conservation Biology Institute; and all the volunteers who
assisted with spotlighting.
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