Using Distance-sampling to Estimate Density of White-tailed
Deer in Forested, Mountainous Landscapes in Virginia
David M. Montague, Roxzanna D. Montague, Michael L. Fies, and Marcella J. Kelly
Northeastern Naturalist, Volume 24, Issue 4 (2017): 505–519
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2017 NORTHEASTERN NATURALIST 24(4):505–519
Using Distance-sampling to Estimate Density of White-tailed
Deer in Forested, Mountainous Landscapes in Virginia
David M. Montague1, 2,*, Roxzanna D. Montague1, Michael L. Fies3, and
Marcella J. Kelly1
Abstract - Although Odocoileus virginianus (White-tailed Deer, hereafter, Deer) are abundant
on private lands throughout much of the western Virginia mountain region, populations
are comparatively low on publicly owned lands in this area. Concerns voiced by sportsmen
regarding declining numbers of Deer on public lands in western Virginia prompted research
to estimate the population density in selected areas within this region. From January 2012
through April 2013, we used ground-based transect sampling with forward-looking infrared
(FLIR) techniques in a distance-sampling framework to estimate seasonal Deer density in
mountainous western Virginia. We included habitat variables and abiotic factors thought
to influence detection and ranked models using AICc model selection in the program
DISTANCE. We observed 430 groups of Deer (mean group size = 2.9) during 5 sampling
sessions conducted along 562.5 km traveled in Bath County, versus 102 groups (mean
group size = 2.6) along 643.6 km in Rockingham County. Wind speed negatively affected
detection, and minimum temperature positively influenced detection. Detection rates were
higher in open areas and forest edges, and higher closer to a full moon. Overall, we found
Deer densities to be lower in the mountainous areas we sampled compared to the few studies
using similar sampling techniques in other nearby areas of the state. Additionally, we
found that while density did not vary seasonally, Deer densities were higher in Bath County
(4.75–16.06 Deer/km2) than in Rockingham County (0.17–3.55 Deer/km2), likely due to the
presence of more edge and open habitat in Bath County. We suggest that distance estimation
is a viable technique to survey Deer, but caution that our sample sizes were small for some
surveys and suggest that future research should seek to account for low detection rates on
national forest lands by increasing effort.
Introduction
Odocoileus virginianus Zimmermann (White-tailed Deer, hereafter, Deer) are
abundant throughout many areas of Virginia; however, Deer populations in parts of
western Virginia, especially on publicly owned lands in the Allegheny Mountains,
are comparatively lower than most other areas of the state (VDGIF 2015). Since the
mid-1990s, Deer population indices on public lands in counties within this region
have declined by 58–71% (VDGIF 2015). During this same time period, the Deer
harvest on public land in western Virginia trended downward by 64% (VDGIF 2015).
Long-term declines in Deer herds on private land have also been documented in the
Alleghany Highland counties (Alleghany, Bath, and Highland) and in the northern
1Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA 24061.
2Downeast Lakes Land Trust, Grand Lake Stream, ME 04668. 3Virginia Department of
Game and Inland Fisheries, Verona, VA 24482. *Corresponding author - dmontague@
downeastlakes.org.
Manuscript Editor: Sonja Christensen
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Shenandoah Valley (VDGIF 2015). Possible reasons for reduced Deer harvests in
this region could include decreasing numbers due to a reduction in habitat quality,
increased mortality due to predation, or alternatively, the harvest decreases could be
due simply to decreases in the number of Deer hunters (Knox 2011).
In the mid-20th century, Deer hunters traveled long distances to the counties
west of the Shenandoah Valley during Deer season, setting up temporary camps
and spending a week or more hunting the large tracts of public land in these areas
(Knox 2011). However, in recent decades, relatively few Deer hunters have traveled
to public lands in these counties, and those that did, reported seeing fewer Deer and
experiencing reduced hunter success (Knox 2011). To some degree, the reduction
in hunters on public lands in western counties corresponds with the decline in the
overall number of hunters in the state. Based on license sales, the number of hunters
in national forests in Virginia has declined by 30% since the mid-1990s (Knox
2011). This decrease in the number of sportsmen utilizing these historically popular
hunting areas was likely influenced by the increased availability of alternative Deer
hunting opportunities in other parts of the state. When hunter numbers were higher
on public lands west of the Shenandoah Valley, Deer populations in many other
parts of the state, such as in southwestern Virginia, were relatively or extremely
low, which is no longer the case (Knox 2011).
Alternatively, the reduced number of Deer hunters on public lands could be
in response to lower Deer densities associated with declining habitat quality. Decreased
timber harvest during the past 30+ years on national forests has resulted
in a dominant habitat type that is of poor quality for Deer: even-aged intermediate
and mature hardwood forests with very little disturbance, few stands of young forest,
and less edge-habitat (VDGIF 2015). The positive association between robust
White-tailed Deer populations and young, disturbed forests and edges is wellestablished
(Roseberry and Woolf 1998, Vreeland et al. 2004, Williamson and Hirth
1985). As forests age and habitat structure becomes increasingly homogeneous,
carrying capacity for Deer declines (Sinclair 1997).
In Virginia, hunter-harvest data (antlered Deer per square mile of forested range)
is used as an index of relative population abundance (VDGIF 2015). This metric is
commonly used to monitor trends in hunted Deer populations with minimal expense
and sampling effort. However, such data have limited utility for estimating actual
Deer population density because hunter harvest fluctuations can result from numerous
variables unrelated to Deer abundance. Deer harvest data can be influenced by
changes in hunter numbers, hunter effort (number of days hunted), variability in
hunter reporting, or hunter success rate due to other variables (i.e., weather, mast
abundance) (Roseberry and Woolf 1991, Rosenberry et al. 2004). To investigate
the current status of Deer populations in areas west of the Shenandoah Valley (Bath
County and western Rockingham County), we employed ground-based distance sampling
(Buckland et al. 2001), with forward-looking infrared (FLIR) techniques, to
estimate seasonal Deer density. This survey method allowed us to directly model and
account for biotic and abiotic factors that might influence Deer detection on transects.
Our objectives were to determine and compare Deer population densities between
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Rockingham County, which is comprised predominantly of public national forest
lands, and Bath County, which contains a mosaic of public national forest land and
small, private inholdings of open and edge habitat. We expected public forest lands
to have lower Deer density because Deer prefer younger, disturbed forests and edges.
We also compared our Deer densities to reported densities in other nearby areas of the
state that used similar sampling techniques. Finally, we offer suggestions for improving
precision of distance sampling for deer in forested habitat.
Field-site Description
Our study areas in Bath and western Rockingham Counties were identified by
the Virginia Department of Game and Inland Fisheries (VDGIF) as priorities for
this research based, in part, on the perception of lower Deer population densities
relative to many other parts of the state (Fig. 1). Land ownership in both areas includes
state, federal, and private holdings.
In Bath County, the study area comprised ~1300 km2 of mosaic landscape that
included forested mountain land under predominately public ownership heavily
interspersed with small, privately owned open land used primarily for pasture and
silage production. Public lands included the George Washington National Forest
(federal), T.M. Gathright Wildlife Management Area (state), and Douthat State
Figure 1. Study areas in 2012 and 2013 for (A) Bath and (B) Rockingham Counties, VA.
Forest is dark gray, open space is light gray, and transects are black lines. Bath County
(89% forested) has more open, private land in the valley bottoms with national forest lands
interspersed along the ridges, whereas Rockingham County (95% forested) has more contiguous
forest habitat with a hard edge to the east formed by agricultural and developed
lands. Rockingham study area depicted within white-dashed polygon.
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Park (state). Public lands are mostly higher-elevation areas consisting mainly
of contiguous, even-aged (≥70 y old), mixed-oak forest, with a few widely dispersed,
small clearings dominated by cool-season grasses and planted Trifolium
spp. (clover). Some modified shelterwood harvesting and prescribed fire is used
in land management.
The Warm Springs Mountain Preserve, a large area of private ownership managed
by The Nature Conservancy (TNC), in Bath County, covers ~37 km2 and
adjoins national forest lands to create an area of roughly 311 km2 (77,000 ac) of
largely unfragmented forest (TNC 2011). There is no timber management within the
TNC preserve, but prescribed fire is used to restore the historic disturbance regime.
With the exception of the Warm Springs Mountain Preserve, private land in Bath
County is concentrated at lower elevations along roadways in the valleys. Private
lands are primarily open and edge habitats or riparian corridors. Much of this area
is managed as farmland or former farmland in various stages of post-agricultural
succession.
The 625-km2 study site in Rockingham County was restricted to the western
third of the county, which is primarily national forest with very few private inholdings,
nearly contiguous forested habitat, and little open land. At the eastern edge
of the Rockingham study site, the forest ends abruptly and transitions into a largescale
agricultural area with livestock pastures, hayfields, and row crops such as corn
and alfalfa (Fig. 1). We did not include this agricultural area in the study; transects
were located at least 1.6 km (1 mi) from the abrupt edge to reduce the influence of
these areas on Deer density estimates.
Methods
We adapted our methodology from those of other distance-sampling studies in
Virginia (Lovely et al. 2013, McShea et al. 2012) except that we also incorporated
forward-looking infrared (FLIR) technology to increase detection probability and
account for mountainous terrain and low Deer detections. FLIR technology detects
body heat, and during our surveys, Deer appeared as bright white objects against
a black background, even when sparse vegetation obscured the outline of the Deer
seen with the naked eye (Fig. 2). We sampled Deer populations during 5 survey sessions:
January, April, and October 2012 and January and April 2013 in both Bath
and Rockingham counties. During the first 2 sessions, we sampled Deer for 4 nights
per county in each session (53–74 km). However, due to low numbers of Deer observed,
we increased sampling effort in the next 3 sessions (Table 1) to include as
many nights of sampling as needed to reach a target number of detections. In the
mosaic landscape of Bath County, we targeted 40 detections on public (closed forest)
and private (open and edge) land, while in Rockingham County we targeted a
total of 40 detections on national forest land. Originally, we had planned to obtain
80 detections per habitat type (as suggested by Buckland et al. 2001), but we were
unable to obtain that goal, even with extensive survey effort. Thus, we note that our
results may not adequately capture variance in detection rates, potentially leading to
imprecise estimates. Although our sampling sessions straddled the 2012–2013 Deer
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Figure 2. Thermal images of White-tailed Deer as seen through forward-looking infrared
(FLIR) imagery in April 2013 in Bath County, VA.
Table 1. Seasonal sampling-effort in Bath and Rockingham counties, VA, from January 2012 to April
2013, including distance surveyed (km), the number of Deer groups, and total number of Deer observed.
Average prop. surveyed = average proportion of open habitats surveys Asterisks (*) indicate
sampling sessions with sufficiently large sample sizes to obtain reliable Deer density estimates in
Rockingham County.
Bath County Rockingham County
Average # of Average # of
Distance # of # of prop. sampling Distance # of # of prop. sampling
Session (km) groups deer surveyed nights (km) groups deer surveyed nights
Jan 2012 53.17 43 133 0.43 4 56.6 2 3 0.04 4
Apr 2012 74.56 91 289 0.38 4 64.82 14 39 0.18 4
Oct 2012 154.99 96 160 0.26 7 183.98* 50 85 0.17 10
Jan 2013 215.77 105 356 0.20 10 158.16 6 10 0.17 10
Apr 2013 83.7 95 401 0.27 6 152.13* 30 63 0.27 6
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hunting season, hunter harvest of antlered bucks per unit area (the statistic used as
a population index by Deer biologists in Virginia; VDGIF 2015) was lower in our
study areas than the average for all counties west of the Blue Ridge Mountains
(i.e., in mountainous Virginia) during this period (Bath: 0.79 antlered bucks/km2,
Rockingham public land: 0.54 antlered bucks/km2, West of the Blue Ridge: 1.01
antlered bucks/km2; VDGIF unpubl. data). Thus, we stratified our results by season
to account for differences in density among seasons due to any factor, including
hunter harvest.
We considered all existing roads within each study area (except major highways
and other high-use paved roads) as transects (Bath: n = 28, Rockingham: n = 18),
and surveyed transects randomly using assigned numbers and a random-number
generator. The majority of roads (98%) were low-use, unpaved forested roads with
complete canopy closure, no cleared median or right-of-way, and no houses, yards,
fields, or other structures that would render them distinct from the surrounding
natural habitat or that would cause Deer to cluster around the road. When necessary,
we repeated sampling of transects, but only after all available transects had
been visited once and only after several days of sampling; thus, no transects were
sampled more than once per night.
We sampled between the hours of 20:00 and 03:00 using 4 x 4 vehicles traveling
at speeds ≤10 km/h. Two observers stood in the bed of the pickup truck searching
opposite sides of the road using the handheld FLIR units (First Mate HM-224, FLIR
Systems, Wilsonville, OR) to detect Deer. To mitigate potential bias that might arise
from movement of Deer from the transect in response to the observer, we used 2 spotters
at all times, avoided unnecessary noise or light, and measured distance to the location
of the first sighting of the Deer if obvious movement away from the observers
occurred. Upon detection, we recorded the vehicle’s location with a handheld GPS
unit, time, weather conditions, air temperature, broadly-classified habitat type, and
the number of Deer detected. We followed Lovely et al. (2013:3) to define a group
of Deer as “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”.
We used a handheld spotlight, laser rangefinder, and a large protractor mounted on the
roof of the truck to obtain the sighting angle from the transect to the Deer. We used the
distance to the Deer or the center point of a group of Deer along the sighting angle to
calculate a perpendicular distance measurement from the transect to the Deer.
To estimate Deer density for both counties and include covariates influencing
detection, we used the multiple-covariates distance-sampling (MCDS) platform
program DISTANCE (Thomas et al. 2009). Covariates of detection included wind
speed (mph), minimum daily temperature (oC), habitat type (forest, pasture, crop
field, edge, riparian), and lunar phase (full, waning gibbous, waning crescent,
new, waxing crescent, waxing gibbous). MCDS uses observation-specific data to
estimate a detection probability; thus, habitat type was the categorical variable
assigned individually to each observation. We tested all covariates singly and in
combination, resulting in more 30 a priori models. Group size was treated using
DISTANCE’s default regression analysis, which reduces the potential bias caused
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by larger groups being detected more frequently at longer distances by plotting
observed cluster-size against distance to estimate average group size. Wind
speed, temperature, and lunar phase were based on archived NOAA weather data
collected at stations located within the study sites (NOAA 2014). If there were too
few detections to warrant covariate testing, we calculated basic density estimates
using the half-normal detection function without covariates. We used a 5% right
truncation of the data to remove outliers according to conventions for analyzing
ground-based linear-survey data (Buckland et al. 2001). We added covariates
in DISTANCE using forward stepwise model-building techniques, and we used
various combinations of key functions, adjustment terms, and bin sizes to achieve
model fit. We had several covariates and a relatively small sample size in some
cases; thus, we made sure to always compare models with covariates to those
without. We ranked models using Akaike’s information criterion corrected for
small sample size (AICc), and competing models were denoted as those within 2
ΔAICs of the top model (Burnham and Anderson 2002). If 95% confidence intervals
on density estimates did not overlap, we assumed that there was a difference
in Deer density between the 2 counties.
To avoid potential habitat sampling bias associated with the use of roads
as transects, we buffered each transect in ESRI ArcMap 10.2.2 with the effective
strip width (ESW) estimated by DISTANCE, averaged among seasons and
stratified by county. We employed the Reclassify tool in ArcMap (ESRI 2014) to
combine landcover types from the 2011 National Land Cover Database (NLCD;
Homer et al. 2015) to calculate the percentages of open and forested habitats
within each transect buffer; these were multiplied by the number of times each
transect was visited in each sampling session to estimate the proportion of open
and forested habitat sampled in each session. We compared these mean proportions
among the transects to the proportion of available habitats within each study
area using 95% confidence intervals.
Results
In all 5 sessions, we detected an adequate number of Deer clusters (n = 43–105;
Table 1) to produce estimates of Deer density in the 130-km2 Bath County study
area. Despite extensive surveying in the 625-km2 Rockingham County study site,
we obtained too few detections in January 2012 (n = 2), April 2012 (n = 14), and
January 2013 (n = 6) to obtain reliable density estimates (i.e., failed chi-square
goodness-of-fit tests for all models). Based on chi-squared goodness-of-fit tests,
we did accumulate enough detections to obtain reliable estimates for Rockingham
County in October 2012 (n = 50) and April 2013 (n = 30).
The mean effective strip width (ESW) was 78.1 m in Bath County (SD = 21.4 m)
and 53.6 m in Rockingham County (SD = 8.7 m). The mean proportions of open
habitats sampled within the ESW were 0.31 (SD = 0.09) in Bath County and 0.17
(SD = 0.08) in Rockingham County. The proportion of open habitat available in
each study area is 0.21 in Bath County and 0.14 in Rockingham County, both of
which fall within the 95% confidence intervals of the proportion s sampled.
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In all seasons for both study sites, the half-normal detection function (with no
adjustments) was highest ranked by AIC compared to other detection functions
(Table 2). Using the default bin sizes led to failure of DISTANCE’s goodness-offit
test; thus, we manually adjusted bin sizes to achieve model fit. In the 5 surveys
in Bath County the half-normal cosine with no adjustments was always the best
detection model as ranked by AICc, but models incorporating habitat, temperature,
wind, and lunar phase were competing, thus demonstrating model uncertainty. In
Rockingham County the half-normal cosine without adjustments was always the
best model, and in 3 of the 5 surveys, no covariates were included in the top-ranked
model with no competing models. In the other 2 surveys, models incorporating
habitat, temperature, and wind were competing (Table 2). In both study areas, covariates
that improved model fit in some seasons included average wind speed and
minimum temperatures. Detection was always negatively related to average wind
speed and positively related to minimum temperature. Detection rate was higher in
open habitats (fields, forest edges, etc.) and higher closer to the full moon.
Density estimates varied from 4.75 to 16.06 Deer/km2 in Bath County and from
0.17 to 4.96 Deer/km2 in Rockingham County (Table 2). Based on the overlap in
the 95% confidence intervals, Deer density did not vary among seasons within each
site, but density estimates were higher in Bath County in 4 of the 5 sessions. Bath
County Deer density varied from 4.3 to 91.3 times higher than in Rockingham
County across seasons (Fig. 3).
Discussion
White-tailed Deer density estimates in our study areas are relatively low
compared to several nearby counties farther east that estimated Deer density using
distance-estimation techniques. Those studies estimated Deer densities of
Figure 3. Seasonal Deer density estimates with 95% confidence intervals for Bath and
Rockingham Counties, VA, from January 2012 to April 2013 as determined by distance
estimation in the program DISTANCE. Asterisks (*)indicate sessions from which reliable
estimates (based on goodness-of-fit tests) were obtained in the Rockingham study area.
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Table 2. Models of White-tailed Deer density (Deer/km2; D), including the lower and upper confidence limits (DLCL and DUCL, respectively) and the
coefficient of variation (DCV) in Bath (BA) and Rockingham (RO) counties, VA, analyzed in the program DISTANCE. Models with ΔAIC > 6 are not
shown. K = the number of parameters, ESW/EDR = the effective strip width (m), P = detection probability, HN = half-normal key function, Cos = cosine
(in all cases no adjustment term was needed), Hab = habitat type, Wind = average wind speed, Temp = minimum temperature, Moon = lunar phase, and
None = no covariates. [Table continued on following page.]
Session/
County Model Covariates K ΔAICc ESW/EDR D DLCL DUCL DCV P Var-P
January 2012
BA HN_Cos Temp 2 0.000 93.755 15.525 6.074 39.682 0.451 0.66968 0.0721
RO HN_Cos None 1 0.000 56.191 0.170 0.000 0.690 0.302 0.45140 0.5402
April 2012
BA HN_Cos Hab 3 0.000 72.562 13.740 7.369 25.619 0.311 0.36281 0.0353
HN_Cos Hab + Temp 5 0.330 67.890 15.024 7.999 28.221 0.316 0.33945 0.0377
HN_Cos Hab + Wind + Moon 5 0.730 69.653 14.483 7.730 27.137 0.314 0.34826 0.0371
HN_Cos Hab + Temp + Wind 5 0.980 70.236 14.104 7.532 26.411 0.314 0.35118 0.0372
HN_Cos Hab + Temp 4 1.126 72.229 13.534 7.256 25.244 0.311 0.36114 0.0355
HN_Cos Wind + Hab 4 1.653 72.565 13.537 7.258 25.246 0.311 0.36282 0.0356
HN_Cos Hab + Moon 4 1.975 72.444 13.836 7.414 25.821 0.312 0.36222 0.0359
HN_Cos Hab + Temp + Wind + Moon 6 2.403 69.375 14.441 7.697 27.095 0.315 0.34687 0.0379
HN_Cos Wind + Moon 3 4.434 75.900 12.120 6.515 22.549 0.310 0.37950 0.0360
RO HN_Cos None 1 0.000 56.373 1.960 0.760 5.050 0.419 0.22689 0.0517
October 2012
BA HN_Cos Wind + Moon + Temp 5 0.000 43.953 12.090 6.593 22.171 0.305 0.35163 0.0387
HN_Cos Wind + Moon 4 4.318 46.648 10.742 5.894 19.578 0.301 0.37319 0.0370
HN_Cos Wind 2 5.611 49.352 9.709 5.358 17.591 0.298 0.39482 0.0346
HN_Cos Wind + Temp 3 5.794 48.238 9.859 5.436 17.882 0.298 0.38591 0.0347
RO HN_Cos None 1 0.000 58.971 2.707 1.502 4.880 0.301 0.53610 0.0678
HN_Cos Hab 3 0.347 57.101 3.161 1.774 5.633 0.294 0.51910 0.0557
HN_Cos Temp 2 1.699 58.731 2.658 1.498 4.717 0.292 0.53392 0.0554
HN_Cos Wind 2 1.928 58.912 2.720 1.534 4.822 0.291 0.53556 0.0544
HN_Cos Wind Hab 4 2.258 57.048 3.161 1.747 5.717 0.303 0.51862 0.0669
HN_Cos Wind + Temp 3 3.647 58.663 2.673 1.504 4.750 0.292 0.53330 0.0566
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Table 2, continued.
Session/
County Model Covariates K ΔAICc ESW/EDR D DLCL DUCL DCV P Var-P
January 2013
BA HN_Cos Wind 2 0.000 98.340 4.749 2.871 7.856 0.257 0.54633 0.0471
HN_Cos Wind + Temp 3 1.952 98.396 4.833 2.911 8.023 0.259 0.54664 0.0502
HN_Cos Wind + Moon 5 5.134 95.202 5.049 1.166 21.858 0.851 0.52890 0.4317
RO HN_Cos None 1 0.000 58.369 1.100 0.21 5.77 0.328 0.42623 0.0359
April 2013
BA HN_Cos Wind 2 0.000 78.853 16.063 8.150 31.660 0.342 0.16702 0.0738
RO HN_Cos Hab 3 0.000 38.238 4.126 1.837 9.267 0.420 0.47797 0.1530
HN_Cos Wind + Hab 4 2.000 37.354 4.313 1.794 10.370 0.458 0.46692 0.1717
HN_Cos Hab + Temp 4 2.000 36.369 4.463 1.627 12.239 0.533 0.45461 0.2079
HN_Cos Hab + Temp + Wind 5 4.000 34.515 4.960 1.705 14.434 0.567 0.43144 0.2140
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9.4–30.1 Deer/km2 (Lovely et al. 2013), 5–47 Deer/km2 (McShea et al. 2008),
and 5.8–33.4 Deer/km2 (McShea et al. 2012). Interestingly, the authors of these
studies did not report difficulty in obtaining adequate sample sizes for distance
estimation and achieved minimum sample sizes in far less time than we did. However,
these studies occurred in parts of northern Virginia where the availability
of suburban and open habitats is greater. VDGIF Deer density indices based on
harvest data (antlered Deer killed per km2 forested range) varied from 1.4 to 1.7 in
these counties, compared to 0.5 on public lands in Bath and western Rockingham
counties (VDGIF 2015).
The positive relationship between Deer abundance and young forests created by
fire or timber harvest, edge habitats, and open agricultural landscapes is well documented
(Beier and McCullough 1990, Roseberry and Woolf 1998, Vreeland et al.
2004). Given this established habitat association for Deer, we were not surprised to
find that Bath County, with a higher proportion of these habitats, had a substantially
higher Deer density than national forest land in Rockingham County. In our first 2
surveys on the Rockingham County land, we had so few Deer detections that we
were only able to fit models with constant detection and no covariates (thus, the
reliability of these estimates should be treated with caution). Hence, we increased
survey effort in Rockingham County in the next 3 sessions (doubling and/or tripling
our effort), yet were only able to include covariates in 2 of these 3 sessions. In the
2 sessions with sufficient detections to incorporate covariates, Deer density was
still significantly lower (95% CIs did not overlap) in Rockingham County than in
Bath County. In fact, of all 5 sessions, only in January 2013 did density CIs overlap
between counties, likely due to low detection of Deer at both sites (relative to survey
effort) and subsequent high model-uncertainty in that winter. Anecdotally, in
Rockingham, Deer were most commonly observed in the few areas that were close
to fields, timber harvests, and agricultural edges—habitats that are uncommon on
the national forest lands. Our study also highlights the need for high expenditure in
sampling effort in future studies in this region to achieve adequate detections for
density estimation in winter.
Detection was often negatively related to average wind speed and positively
related to minimum temperature in our models; thus, our inability to achieve an
adequate number of detections in mid-winter may have been due to abiotic factors.
It is possible that cold, windy weather reduced Deer movements, as observed by
Schmitz (1991). Such a reduction in movement could affect the detectability of
Deer during distance sampling. Alternatively, Deer in western Rockingham County
may have reduced their use of forested habitats during the winter months in favor
of nearby open habitats with higher food availability. Winter movement toward
agricultural row crops was observed by Brinkman et al. (2005) in Minnesota, and
both Storm et al. (2007) and Kilpatrick and Spohr (2000) documented Deer movement
toward human dwellings in winter. These more open or early-successional
habitats were typically found on private lands adjacent to the national forest lands
we sampled, and if such a habitat shift occurred, our low estimates accurately
reflect deer densities in western Rockingham County in winter. Interestingly, our
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Deer-detection rate increased close to the full moon, which may indicate higher
Deer activity levels at this time due to enhanced visual detection of predators.
We note that the use of roads as transects can bias density estimates if Deer are
attracted to or avoid roads (Buckland et al. 2001). For example, if open or agricultural
habitats are disproportionately located in close proximity to roads, deer may
be drawn to these habitats, resulting in inflated estimates of density. This bias is
unlikely in our Rockingham County study area because the available roads used as
transects were almost exclusively narrow, dirt roads used for recreational access
within the national forest that were not associated with agriculture, houses, or associated
open areas. Rights of way seeded with vegetation occurred rarely, and we
did not observe Deer using these areas when they were available. Given the low
intensity of human use of these roads, it is also unlikely that bias was introduced
by Deer avoiding roads. Thus, sampling on such low-use roads was the best option
to effectively cover the 2 study areas.
Our estimates of Deer density in Bath County (4.75–16.06 deer/km2) are consistent
with crude density estimates inferred from harvest data (calculated as roughly
10 times the number of antlered bucks killed per square mile) reported by VDGIF
in Bath County (density estimate of ~10.0 deer/km2; VDGIF 2015). The lack of seasonal
differences in population density may be due to the relatively short duration
of the study and a corresponding low sample size for comparisons. Comparisons of
Deer density among years may reveal differences over longer time periods, particularly
when comparing years during which food availability varied (i.e., years with
high abundance of acorns versus years with acorn crop failure. Our estimates are also
consistent with VDGIF observations that Deer density indices in Bath County (public
and private lands combined) were higher than on public lands in western Rockingham
County. Despite low Deer densities, particularly for Rockingham County, we
were able to document significantly lower density estimates in Rockingham County
via distance sampling, especially once we increased sampling effort.
In addition to being consistent with Deer density-index data (VDGIF 2015), our
data also support anecdotal hunter reports of very low Deer density on public lands
in the areas we studied. The cause of these low densities remains unclear; however,
the higher density that we observed in Bath County likely correlates with greater
availability of open and disturbed habitats that Deer are known to favor (Johnson
et al. 1995, Nixon et al. 1991). This conclusion is supported further by our observation
that a majority of Deer were detected in open and disturbed habitats in all
seasons in both study sites.
Although distance sampling is widely used to study ungulates in other parts of
the US (Koenen et al. 2002, LaRue et al. 2007, Ward et al. 2004), it has not been
used often for White-tailed Deer in Virginia. Aerial distance-sampling with infrared
imagery can be cost prohibitive, and may be limited primarily to sampling in deciduous
forests with low topographical relief during the winter months (Beaver et
al. 2014, Kissell and Nimmo 2011, Storm et al. 2011). Ground-based distance sampling
with FLIR has been used successfully for multi-species sampling in mixed
habitats (Morrelle et al. 2012) and in areas where precise estimates are needed for
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focused management plans (Techentin et al. 2012). Our study demonstrates the utility
of ground-based distance sampling with FLIR in a mountainous area with mixed
coniferous and deciduous forest during non-winter seasons. Distance sampling at a
county scale avoids issues surrounding the prevailing use of harvest data to estimate
trends in Deer numbers, and while distance sampling has its own assumptions and
limitations, we have shown that it can be a viable alternative tool for estimating
and comparing densities across habitats. We caution however, that low-density
Deer populations require extensive effort to obtain enough detections to reliably
estimate density, and researchers should plan accordingly.
Acknowledgments
Primary funding for this research came from the Virginia Department of Game and
Inland Fisheries through a Pittman–Robertson Wildlife Restoration Grant. Additional support
was provided by The Nature Conservancy, the Virginia Deer Hunters Association, the
USDA Forest Service, and the Department of Fish and Wildlife Conservation at Virginia
Tech. We wish to thank the many private landowners who allowed us to sample Deer populations
on their properties. Special thanks to VDGIF Deer Project Leader Nelson Lafon for
providing necessary sampling equipment, field-sampling assistance, and manuscript review.
We also appreciate the numerous technicians, volunteers, and student researchers who assisted
with fieldwork.
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