The Journal of Wildlife Management; DOI: 10.1002/jwmg.712
Research Article
Targeting Mitigation Efforts: The Role of
Speed Limit And Road Edge Clearance for
Deer–Vehicle Collisions
ERLING L. MEISINGSET, Norwegian Institute for Agricultural and Environmental Research, Organic Food and Farming Division, NO-6630,
Tingvoll, Norway
LEIF E. LOE, Department of Ecology and Natural Resource Management, Norwegian University of Life Science, P.O. Box 5003 NO-1432,
Aas, Norway
ØYSTEIN BREKKUM, Norwegian Institute for Agricultural and Environmental Research, Organic Food and Farming Division, NO-6630,
Tingvoll, Norway
ATLE MYSTERUD,1 Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, P.O. Box 1066
Blindern NO-0316, Oslo, Norway
ABSTRACT Deer–vehicle collisions (DVCs) have major social, economic, and animal welfare costs.
Modeling of accident risk relative to environmental characteristics could enable managers to plan the layout
of new roads or determine optimal location of mitigation measures to reduce the number of accidents on
existing roads. We analyzed position data providing road crossings from 67 global positioning system (GPS)marked red deer (Cervus elaphus) and location data of 271 car killed deer within the home ranges of the GPSmarked red deer. We modeled collision risk as a function of speed limit, season, road characteristics, and
habitat features. For a subset of the data, we tested if vegetation clearance along a highway reduced the
collision frequency of red deer. The relative risk for DVCs increased with speed limit. We found a higher risk
for DVCs during winter compared to the other seasons. Forest cover, distance to pasture, and terrain
ruggedness substantially affected risk of DVCs. Road edge clearance reduced the frequency of DVCs, but the
effect appeared in the winter season only with a decrease of 53%. Our study highlights that speed limit
reduction and road edge clearance are both powerful mitigation tools to reduce the number of DVCs. Ó 2014
The Wildlife Society.
KEY WORDS Cervus elaphus, collision risk model, deer–vehicle collisions, mitigation, red deer, road edge clearance,
speed limit.
Many cervid populations in Europe and the United States
have increased dramatically in recent decades (McShea and
Underwood 1997, Putman et al. 2011). At the same time, the
traffic level has increased and road network has developed
substantially. This has led to increased frequency of deer–
vehicle collisions (DVCs), now counting several million
each year around the world (Groot Bruinderink and
Hazebroek 1996, Rea 2003, Mysterud 2004, Dussault
et al. 2007, Langbein et al. 2011), and is a major safety issue
both to humans and wildlife (Groot Bruinderink and
Hazebroek 1996, Langbein et al. 2011). Direct costs related
to human mortality and injury, animal welfare, and material
damage are high and in many regions, loss of potential
hunting objects introduces indirect costs. Since DVCs are
often traumatic for animals and humans and economically
costly as well, more effective mitigation measures along roads
are urgently needed (Langbein et al. 2011). Analyses of
Received: 17 June 2013; Accepted: 23 February 2014
1
E-mail: [email protected]
Meisingset et al.
Collision Risk of Red Deer
temporal and spatial patterns of risk for DVCs may
contribute with important information to target relevant
mitigation measures for the future.
The number and variety of mitigation measures to reduce
DVCs have increased greatly during recent decades (Iuell
et al. 2003), but many of the mitigation measures have not
been evaluated scientifically (Langbein et al. 2011). Use of
fences or road under- or overpasses (also in combination) can
reduce DVCs substantially (Clevenger et al. 2001, Langbein
et al. 2011), but these are expensive measures, difficult to
implement on a larger scale, and often seriously hamper
animal movement. Other mitigation measures cannot reduce
DVC to the same extent, but they usually aim to reduce the
number of accidents to socially and economically acceptable
levels.
The spatial and temporal distributions of wildlife accidents
are not random (Seiler 2005, Gunson et al. 2011). Landscape
and habitat features may play an important role in
determining where cervids cross roads, and risk zones for
DVCs are often associated with a number of factors
(Dussault et al. 2006, Danks and Porter 2010, Gunson
et al. 2011). Roads are often associated with habitats
1
containing nutritious forage both for browsing and grazing
herbivores. Access to selected habitats can reduce road
avoidance behavior of cervids (Gagnon et al. 2007) and
increase the rate of road crossings (Meisingset et al. 2013).
High traffic levels can reduce crossing frequency, but the
level has to be very high to prevent crossings completely
(Gagnon et al. 2007). A necessary step toward understanding
factors causing accidents is to quantify risk (Seiler 2004,
Langbein et al. 2011). Risk is more than a question of animal
behavior in relation to road networks. It also involves the
drivers’ behavior in terms of speed and ability to observe deer.
Though speed limit is likely a key factor, only a few reports
have quantified how speed limits affect the number of DVCs
(Bashore et al. 1985, Seiler 2005, Danks and Porter 2010,
Neumann et al. 2012).
Predictive models of DVC sites are regarded as the key to
mitigate accidents both at regional and local scales (Malo
et al. 2004, Gunson et al. 2011). Such models can be
important for identifying relevant factors for DVC risk and
to assess mitigation measures at the right scale. Modeling of
accident risk relative to environmental characteristics could
enable managers to plan the layout of new roads or determine
locations of mitigation measures. One commonly used
mitigation measure is clearance of vegetation along roads to
prevent collision in forest habitats (Rea 2003, Langbein
et al. 2011). The idea is to increase the visibility of
approaching animals and give the driver more time to react.
Removal of palatable vegetation also reduces the time
animals spend in the vicinity of the road and thereby
decreases the number of potential crossings (Olsson 2007).
This measure is commonly used to prevent moose (Alces
alces)-vehicle collisions (Iuell et al. 2003, Rea 2003), but few
studies have formally tested the effect of vegetation clearance
along roads for reducing DVCs (Gunson et al. 2011,
Langbein et al. 2011).
Red deer (Cervus elaphus) is among the most widely
distributed large herbivore species in Europe (Skog
et al. 2009), and populations have increased in number
over much of Europe depending on the management system
(Milner et al. 2006). In Norway, 840 red deer were recorded
killed in car accidents during the season 2007–2008, which
was historically high. The main reason for the increase in
number of car-killed red deer in Norway seems to be an effect
of increasing deer population size and increasing traffic
burden (Mysterud 2004). We developed 2 collision risk
models by contrasting location data of DVCs with 1) random
positions along roads, and 2) position data from global
positioning system (GPS)-marked red deer within the study
area in Møre & Romsdal and Sør-Trøndelag, Norway. We
developed models of relative risk of collisions by incorporating information about speed limit, road standard, and habitat
features. Further, we tested the effect of vegetation clearance
along a highway on collision frequency of red deer within 1
part of the study area.
STUDY AREA
The study area covered approximately 24,000 km2 overlapping several municipalities in the counties of Møre &
Romsdal and Sør-Trøndelag in central Norway (N 62.15–
63.598; E 6.82–10.698; Fig. 1; Fig. A1, available online at
Figure 1. Examples of distribution of home ranges of red deer (100% minimum convex polygons; solid black lines: n ¼ 24) with major roads (gray lines) and
road crossings (dark gray dots) within the study area in central Norway, 2007–2008.
2
The Journal of Wildlife Management
9999
www.onlinelibrary.wiley.com). The climate is oceanic and
precipitation and temperature generally declines from coast
to inland and from lowland to higher altitudes. Snow cover is
normally present during winter, but depth and duration of
snow cover increases from coast to inland and with altitude,
and is highly variable among years (Mysterud et al. 2000).
The topography is characterized by valleys and fjords, hills,
and mountains, with peaks reaching more than 1,500 m
above sea level. Agricultural areas are normally situated on
flatter and more fertile grounds in the bottom of valleys. The
study area was forested and situated in the south to middle
boreal zone (Moen 1998). A more detailed description of the
vegetation can be found elsewhere (Meisingset et al. 2013).
Highways (termed major roads) and country roads (termed
medium roads) typically followed flatter areas in valley
bottoms and frequently passed through agricultural land
where red deer feed (Godvik et al. 2009, Meisingset
et al. 2013). Private roads (termed small roads) were located
around houses, farms, and pastures, and within forested areas
where they mainly were built for timber transport. Road
density for major, medium, and small roads was 0.05, 0.10,
and 0.22 km/km2, respectively. Density of roads was highest
in agricultural and high productive forest areas (Meisingset
et al. 2013). The area had no fencing along the roads to
prevent DVCs. Road edges were cleared along a highway in
the valley of Sunndal, in the southern part of our study area.
The control area was situated in the neighboring valley
Surnadal with a comparable topography, highway, traffic
load, and red deer population density. These 2 valleys were
typical Norwegian fjord valleys with high mountains and had
a highway passing through the valley beside a river.
The red deer population is located in the core areas for red
deer in Norway (Mysterud et al. 2011). It has increased
steadily the last 4 decades and the harvest yield has increased
approximately 6-fold in the region as a whole during this
period, and more than 6,300 red deer were harvested during
autumn hunt in 2008. Annual harvest takes place between 10
September and 23 December. A majority of adult red deer
migrate seasonally between small winter (coast; low
elevation) and summer (inland; higher elevation) ranges
(Bischof et al. 2012), but the proportion of migrants varies
with population density within the municipality and local
topography (Mysterud et al. 2011). Within our study area,
140–160 red deer were killed by car per year, at an increasing
rate during the last 2 decades (Meisingset et al. 2010).
METHODS
Data Collection
We obtained data on DVCs within the study area from the
Cervid Register in Norway. We had access to data from 271
DVCs within the combined 100% minimum convex polygon
home range of the GPS-marked red deer. We used 100% to
be sure to include the entire area where road crossings
occurred. The DVCs had exact locations (recorded with
handheld GPS; accuracy 10 m) and date (ranging from Jan
2006 to Jun 2009). During summer 2008, vegetation removal
was conducted along a 32-km stretch of a highway in our
Meisingset et al.
Collision Risk of Red Deer
study area (Highway 70 in the valley of Sunndal). All
vegetation up to 8 m from the road shoulder was removed to
increase visibility along the road edges. In the subsequent
summers, new vegetation in this clearance area was cut
mechanically to prevent re-growth. Deer–vehicle collision
data along this highway section were collected from the
period 2003–2010, totaling 137 collisions (103 occurring
before and 34 occurring after clearance). We excluded
3 months in summer 2008 when the vegetation removal was
conducted, which gave 94 months of data. Data on DVCs
were also collected from the 35-km control stretch (Highway
65 in Surnadal) located about 25 km from the treatment road
in the neighboring municipality. In the control area, 70
DVCs occurred on the highway during the study period.
We used individual GPS data from red deer to identify
crossing sites. We had access to 67 adult (2 years) red deer
of both sexes (45 females and 22 males) marked with GPS
collars (Followit AB, Lindesberg, Sweden; Meisingset
et al. 2013). We caught animals by darting on winter sites
from January to April in 2007 and 2008, after a procedure
approved by the national ethical board for science. Winter
feeding sites for marking were spatially distributed in
lowland natural wintering areas within the study area. Most
animals use winter-feeding sites and we regard the possibility
for a biased sampling within the deer population to be low,
but in winter each deer will often be found close to the site it
was captured. Our marking sites differed in relation to road
densities and population densities. We selected individuals
for capture based on proximity to the hidden capture
personnel and position within the group (to facilitate a safe
shot). Female collars recorded a position every hour, whereas
male collars recorded positions every second hour apart from
10 September to 1 December when male collars recorded
positions every hour. The average fix success rate was 82.5%
(range 61.0–98.5%). We deleted locations obtained during
the first 24 hr after marking. After a screening process that
included visually plotting for removal of outliers and speed
rules (deleting points that indicated a movement speed of
more than 30 km/hr), 370,859 locations were available for
further analysis. Deleted points composed less than 0.5% of
all locations. Throughout the 2 years of data sampling, a
mean of 34.8 (SD 10.66) collars were operational every
month, and each GPS collar obtained positions for 11.8
(SD 5.54) months. The mean number of locations per
individual was 5,454 (SD 2,784.4). Median location error
for our GPS collars was 12 m (Godvik et al. 2009). The deer
home ranges were spread out over a large area, varied largely
in size, and had substantial overlap in the areas with the most
intensive catching effort (Fig. A1).
We defined deer road crossing sites where step lines
intersected a road (see Meisingset et al. 2013 for a closer
description). To increase precision of estimated road crossing
sites, we excluded all crossings between GPS locations with a
sampling interval >120 min (due to missing positions before
or after crossing). We also excluded crossing sites in cases
where the distance between successive positions exceeded
1 km. This resulted in 34,360 remaining crossing sites
available for further analyses. The GPS-sampling interval or
3
distance between locations did not likely result in any
systematic bias from excluded crossing points.
We divided the roads into 3 categories based on size and
expected traffic burden according to the Norwegian road
authority’s classification of roads (Meisingset et al. 2013).
We classified private roads as small roads because of low
traffic burden (estimated cars/day: 0–100) and just 1 unpaved
lane; country roads as medium roads because these were
usually paved and with 1 lane in each direction and with
intermediate traffic burden (estimated cars/day: 100–1,000);
and highways as major roads with at least 1 lane in each
direction and high traffic burden (estimated cars/day: 1,000–
4,000; www.vegvesen.no). Roadmaps were available as vector
layer data at scale 1:50,000.
Speed limits on all roads were available from the
Norwegian road authority, which covered all registered
roads within our study area. Speed limits varied from 30 to
80 km/hr. Because speed limits of 30 km/hr and 40 km/hr
mainly were distributed within or close to urban areas, which
is likely to influence the crossing behavior of red deer, we
excluded these road segments from the analyses totaling 171
crossing sites and 2 DVC sites. Very few stretches of roads
with 70 km/hr speed limits occurred within the study area;
we therefore grouped 60 and 70 km/hr speed limits and
treated them as 1 level.
We characterized the habitat in a 100-m circular buffer
zone around each crossing site. We selected the size of the
buffer zone to realistically include the actual crossing site,
and to be large enough to allow characterization of the
topography but small enough to provide a useful measure of
habitat composition in our finely fragmented study area.
Within the buffer zone, we calculated the proportion of
different habitat types. We derived habitat types from digital
land resource maps provided by the Norwegian Forest and
Landscape Institute, with scale 1:5,000 (Godvik et al. 2009,
Loe et al. 2012, Meisingset et al. 2013). We classified habitat
into agricultural infields (mostly pastures and meadows;
termed pastures hereafter) and forest, using distance to
pasture (linear distance in meters from the estimated crossing
site to the closest pasture) and proportion of productive forest
cover within the 100-m buffer as covariates in subsequent
analyses. We calculated the same variables for 100-m buffers
around DVC sites. We also quantified terrain ruggedness
based on a digital elevation model with 25-m resolution. For
each grid cell, we calculated a ruggedness index (Sappington
et al. 2007) based on the center cell and the 8 neighboring
cells (i.e., the values are based on the elevation in a 75m 75-m square). High scores represented a highly variable
topography, whereas lower scores represented a smoother
terrain. We processed spatial maps and data in ArcGIS 9.3
(Environmental Systems Research Institute, Inc., Redlands,
CA). To calculate distance to pastures, we used the vectorediting tool, Snap Points to Lines, in Hawth’s Analysis Tools
for ArcGIS (Beyer 2004). To calculate terrain ruggedness,
we used Terrain Tools for ArcGIS.
As an index of population density within Sunndal (location
of the vegetation clearance) and Surnadal (control site), we
used the annual number of harvested red deer during the
4
hunting season (obtained from the local managers in Sunndal
and Surnadal municipalities). The number of shot animals
gives a fairly good estimate of changing population density
between years within a given area (Mysterud et al. 2007). We
gathered data on snow depth (measured as max. snow depth
during the specific month) from local weather stations in the
2 municipalities (stations no. 64,580 and 64,800, www.
eklima.no).
Statistical Analyses
Predictive models of collision risk.—To test if DVC sites
differed from random locations along roads and from red
deer crossing sites, we fitted 2 different exponential resource
selection function (RSF; Equation (1)) models with
maximum likelihood estimates and nonparametric bootstrap
standard errors (B ¼ 99), using the function “rsf” in the
package “ResourceSelection” in R (Lele 2009, Lele
et al. 2013, McDonald 2013).
wðxÞ ¼ expðb1 x1 þ þ bp xp Þ
ð1Þ
In the first case, we fitted an RSF model with DVC (coded
1) and random locations (coded 0) as the response variable
with a non-matched used-available design. In the second
model, we fitted an RSF with collision site (coded 1) and deer
crossing site (coded 0) as the response variable (Table A1). In
the second model, each DVC site was named after the
individual red deer home range(s) it was located within. Both
DVC site and deer crossing site were therefore associated to
an individual deer, and individual red deer home range was
included as a matching factor in a matched used-available
RSF. Candidate predictor variables were the same in both
models (Table A2, available online at www.onlinelibrary.
wiley.com) and included season, road category, distance to
closest pasture, terrain ruggedness, productive forest cover,
and speed limit. Seasons included spring (1 Apr–31 May),
summer (1 Jun–15 Aug), autumn (16 Aug–30 Nov), and
winter (1 Dec–31 Mar) and was incorporated as a 4-level
factor variable. We divided road category into major and
medium sized roads as a 2-level factor variable. Because no
DVCs occurred at small roads, but 55.0% of red deer
crossings occurred on these road segments, we excluded red
deer crossing sites at small roads from the analyses. We
treated distance to closest pasture as a 2-level factor variable,
closer or farther than 50 m, to investigate the local effect of
improved visibility in open terrain. We treated terrain
ruggedness as a numeric variable; the value increased with the
degree of ruggedness. We treated productive forest cover as a
numeric variable from 0% to 100% cover of productive forest
within the 100-m circular buffer zone. And finally, we
included speed limit as a 3-level factor variable with 50, 60–
70, or 80 km/hr as levels.
This type of model has been termed a collision risk model
(Malo et al. 2004, Seiler 2005, Frair et al. 2008). In our case,
this gives a relative probability of a site being a collision site
because 2 different datasets are used to estimate the
probability. We collected road crossings only for the subset
of GPS-marked deer. Because only 1 GPS-marked deer was
killed by a car, the collision data consisted of all reported red
The Journal of Wildlife Management
9999
Meisingset et al.
Collision Risk of Red Deer
0.12
0.22
þ
0.31
0.04
0.78
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
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þ
þ
þ
þ
þ
1
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
1
0.65
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
1
þ
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þ
þ
1
þ
þ
þ
þ
þ
þ
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þ
þ
þ
1
Model
1
2
3
4
5
6
7
8
9
10
Imp.
þ
þ
Weight
0.202
0.182
0.097
0.076
0.068
0.046
0.041
0.036
0.035
0.032
0.00
0.22
1.47
1.95
2.19
2.96
3.18
3.43
3.51
3.66
DAIC
AIC
3,952.7
3,952.9
3,954.2
3,954.7
3,954.9
3,955.7
3,955.9
3,956.1
3,956.2
3,956.4
1,966.4
1,963.5
1,958.1
1,966.3
1,963.5
1,961.8
1,969.9
1,958.1
1,967.1
1,969.2
logLik
Df
10
13
19
11
14
16
8
20
11
9
þ
þ
þ
þ
þ
þ
þ
þ
þ
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1
Road
category speed limit
Season road
category
Season speed limit
Road
category distance
to pasture
Speed
Season dis- limit Season
Speed limit
Terrain Road category
tance
distance
(categorical) (categorical) ruggedness (categorical) to pasture to pasture
Cover of
productive
forest
Out of the available 271 DVC sites, 83.0% (n ¼ 225) were
found on major roads and 17.0% (n ¼ 46) on medium-sized
roads. None were found on small roads. Out of the 34,360
identified red deer road crossing sites, 11.9% (n ¼ 4,075)
were found on major roads, 33.1% (n ¼ 11,386) on medium
roads, and 55.0% (n ¼ 18,899) on small roads (see descriptive
statistics in Tables A1 and A2, available online at www.
onlinelibrary.wiley.com).
Distance to
pasture
(categorical)
RESULTS
Table 1. Degrees of freedom (Df), log likelihood (logLik) value, Akaike’s Information Criterion (AIC) values, DAIC values, and weight for the top 10 candidate models (based on AIC) for red deer–vehicle collision
sites (DVC, coded 1) versus random sites along roads (coded 0) in central Norway, 2006–2009, fitted with an exponential resource selection function (RSF) model with maximum likelihood estimates. For each model
(1–10), þ shows whether the specific parameter is included into the model. The total number of candidate models was 339. The relative importance of variables (Imp) is provided in the last row.
deer (with 1 exception unmarked) killed by car within the
combined home ranges of the GPS-marked deer. The low
proportion of GPS-marked deer DVC sample is likely
explained by a low number of GPS-marked individuals
compared to the total population size (of mainly unmarked
deer) that are subjected to DVCs. We also selected random
sites along the roads within the combined deer home ranges.
We tested for correlation among the explanatory variables.
However, no variable was correlated by r > 0.5, so all
candidate variables were subjected to model selection. We
selected candidate models using the dredge function in the
package “MuMIn” in R. In the first model (DVC vs.
random), we included all predictor variables, as well as all 2way interactions between the categorical variables. In the
second model (DVC vs. crossings), we included all the
predictor variables, as well as the 2-way interaction between
road category and speed limit and season and distance to
pasture. We selected the final models based on Akaike’s
Information Criterion (AIC). We also calculated model
weights and relative importance of the variables (sum of
Akaike weights of all models where the variable is included;
Barton 2013) in the top-ranked candidate models. Even
though we had data from both sexes, we could not analyze if
1 sex was overrepresented in DVCs because we had no
precise information about sex ratio within the whole study
population. In the full model, we included all DVC sites
including deer of unknown sex (which substantially increase
sample size).
Effect of vegetation clearance.—We tested the effect of
vegetation clearance on Highway 70 by using a negative
binomial generalized linear model using the function glm.nb
in library MASS in R (Venables and Ripley 2002). The
response variable was the number of DVCs per month
(n ¼ 94 months). Predictor variables included the 2-level
factor variables treatment (treatment area and control area),
period (before and after vegetation clearance in the treatment
area), and season (winter, dark season with snow [Oct–Mar],
and summer, light season and no snow [Apr–Sep]), as well as
population density (numeric) and the interaction between
treatment and period. We also developed a separate winter
model (a subset of data from the winter season [Oct–Mar])
where we also included snow depth (numeric; measured as
maximum registered snow depth each month). We ran the
last model to control for the effect of snow conditions during
winter, which may lead to more slippery roads and a higher
concentration of deer density in the valley bottom with more
road crossings as a result. We conducted all analyses in R
version 2.15.2 (R Development Core Team 2011).
5
Red Deer–Vehicle Collision Sites
The distribution of DVCs compared with random sites along
roads differed considerably in relation to several of the
predictor variables. The most supported model (Table 1)
included road category, distance to pasture, productive forest
cover, terrain ruggedness, and speed limit, as well as the 2way interactions between road category and speed limit, and
distance to pasture and speed limit. The relative risk for
DVCs increased with speed limit (Table 2). With a change in
speed limit from 50 to 60–70 km/hr and from 50 to 80 km/
hr, relative risk increased by 3.9 and 8.6, respectively. The
increase in relative risk from 50 to 80 km/hr was lower for
medium than for major roads (interaction between road
category and speed limit; Table 2). An increasing proportion
of productive forest cover increased the risk for DVCs
(Table 2). With a 10% increase in cover of productive forest,
the relative risk increased by 1.3. We also observed an
increased relative risk for DVCs in areas with more rugged
terrain (relative risk is 7.4 times higher in the road locations
with the lowest compared to the highest ruggedness values;
Table 2).
The DVC sites differed from red deer crossing sites for
several of the predictor variables. The most supported model
included speed limit, road category, season, distance to
pasture, and productive forest cover, as well as the 2-way
interactions between road category and speed limit, and
season and distance to pasture (Table 3). The relative risk
for DVCs increased with speed limit and was highest at
80 km/hr (Table 4, Fig. 2). An increase in speed limit from
50 to 60–70 km/hr increased the relative risk by a factor of
6.8, whereas from 50 to 80 km/hr gave an increased risk of
14.4. The relative risk for DVC was lower at medium-sized
roads compared to major roads (relative risk of 0.3). As for
the previous model, collision risk increased more at major
roads compared to medium-sized roads when increasing the
speed limit from 50 to 80 km/hr (Table 4). The relative risk
decreased considerably when distance to pasture exceeded
50 m (relative risk of 0.4; Table 4, Fig. 2) and this difference
occurred in all seasons. Probability of a DVC during winter
was considerably greater compared to the other seasons
(Table 4, Fig. 2). The increase in relative risk from autumn
to winter was 4.9, whereas risk was lower in spring
compared to autumn (relative risk of 0.6). An increasing
level of productive forest cover increased the risk for DVCs
(Table 4, Fig. 3). Relative risk increased by 1.3 for every 10%
increase in cover of productive forest. Relative risk increased
at a higher rate closer to pastures and in winter compared to
other seasons with an increasing level of productive forest
cover (Fig. 3).
Effect of Vegetation Clearance
The mean number of DVCs per month decreased by 31%
from 1.6 (SD ¼ 2.00, n ¼ 64) before to 1.1 (SD ¼ 1.33,
n ¼ 30) after vegetation clearance in the treatment area. This
amounts to 6 DVCs less per year for the 32-km road stretch.
In the control area, DVCs increased from 0.6 (SD ¼ 0.77,
n ¼ 64) per month to 1.1 (SD ¼ 1.59, n ¼ 30) over the same
period for the same length of road. The effect of vegetation
clearance differed by season and reduced the number of
DVCs during the winter season only (Table 5, Fig. 4), when
the reduction in DVCs was as high as 53%. Population
density tended to be positively related to number of DVCs.
The winter model confirmed the positive effect of road edge
clearance in the treatment area (Table 5), even after
controlling for effects of snow cover and population density
on collision risk.
DISCUSSION
Temporal and spatial patterns in DVCs can be used to
predict the optimal locations of deer crossing structures (sites
with high risk of DVC; e.g., Clevenger and Waltho 2005),
the seasonal timing of efforts, and how to manipulate habitat
features or road characteristics to reduce collision risk. Our
study shows that both spatial and temporal (seasonal)
variation occurs in collision risk by red deer, and that several
factors contribute to this risk. Speed limit is important for
risk of DVC. Further, road edge clearance substantially
reduced DVC frequency and efforts could be targeted to the
winter season.
Increased speed gives the driver shorter time to respond
when a deer crosses the road in front of the car, and increased
distance for the vehicle to stop. Studies on collision sites of
moose both in Sweden and western Maine, USA, have
previously reported higher probability of moose–vehicle
collisions with increasing speed limit (Seiler 2005, Danks
and Porter 2010, Neumann et al. 2012). Danks and Porter
Table 2. Parameter estimates and test statistics from the most parsimonious model (exponential resource selection function [RSF] model with maximum
likelihood estimates and nonparametric bootstrap standard errors [B ¼ 99]) for the risk model contrasting red deer–vehicle collision (DVC) sites (coded 1)
with random sites along roads (coded 0) in Møre & Romsdal and Sør-Trøndelag, Norway, 2006–2009. Estimates of factor variables are relative risk relative
to a reference category, whereas estimates for numeric variables are the change in relative risk per unit of the variable (1% for productive forest cover).
Parameter
Road category (medium vs. major)
Distance to pasture (>50 m vs. <50 m)
Productive forest cover (%)
Terrain ruggedness
Speed limit (60 vs. 50 km/hr)
Speed limit (80 vs. 50 km/hr)
Road category (medium) speed limit (60 km/hr)
Road category (medium) speed limit (80 km/hr)
Distance to pasture (>50 m) speed limit (60 km/hr)
Distance to pasture (>50 m) speed limit (80 km/hr)
6
Estimate
SE
Z
Lower 95% CI
Upper 95% CI
0.343
0.116
0.012
1.744
1.374
2.147
0.974
2.278
0.084
1.153
0.542
0.980
0.002
0.371
0.569
0.553
0.644
0.614
1.103
0.982
0.633
0.118
5.112
4.696
2.415
3.881
1.512
3.709
0.076
1.175
1.404
2.036
0.008
1.016
0.259
1.063
0.288
3.481
2.246
3.078
0.719
1.804
0.017
2.472
2.489
3.232
2.236
1.074
2.079
0.771
The Journal of Wildlife Management
9999
1
0.22
þ
þ
Weight
0.777
0.223
0.000
0.000
0.000
0.00
2.49
18.51
18.68
44.39
DAIC
AIC
4,131.7
4,134.2
4,150.2
4,150.4
4,176.1
2,052.9
2,053.1
2,065.1
2,064.2
2,079.1
logLik
Df
13
14
10
11
9
þ
þ
þ
þ
þ
1
þ
þ
þ
þ
þ
þ
þ
1
þ
þ
þ
þ
þ
1
1
þ
þ
þ
þ
þ
1
1
2
3
4
5
Imp.
Model
þ
þ
þ
þ
þ
þ
þ
þ
þ
1
Road
category speed limit
Season distance to
pasture
Road
category
(categorical)
Terrain
ruggedness
Season
Speed limit
(categorical) (categorical)
Cover of
productive
forest
Distance to
pasture
(categorical)
Table 3. Degrees of freedom (Df), log likelihood (logLik) value, Akaike’s Information Criterion (AIC), DAIC values, and weight for the top 5 candidate models (based on AIC) for red deer–vehicle collision sites
(DVC, coded 1) versus red deer crossing sites (coded 0) in central Norway, 2006–2009, fitted with an exponential resource selection function (RSF) model with maximum likelihood estimates. For each model (1–5), þ
shows whether the specific parameter is included into the model. The total number of candidate models was 76. The relative importance of variables (Imp.) is provided in the last row.
Meisingset et al.
Collision Risk of Red Deer
(2010) found that for each 8 km/hr increase in speed, odds of
moose–vehicle collisions increased by 35%, but the rate of
increase was most apparent at speed limits above 72 km/hr.
Also in our case, speed limit was an important factor to
explain variation in relative risk of DVC.
The timing of mitigation efforts is critical when considering costs of maintenance (as for road clearance) or feasibility
of continuous efforts. For example, reducing speed limit may
be unpopular among car drivers, but may work better if it is
only for given periods of particularly high risk. The risk of
DVC shows considerable temporal variation, but we found
no consistent pattern among studies. Studies have reported
peaks in DVC during autumn (roe deer [Capreolus capreolus];
Madsen et al. 2002), summer (moose; Danks and
Porter 2010, Neumann et al. 2012), or during migration
periods (moose; Dussault et al. 2007), whereas some studies
have shown a peak of road crossings during rutting season
and hunting activities (white-tailed deer [Odocoileus virginianus]: Allen and McCullough 1976, Sudharsan et al. 2006;
moose: Lavsund and Sandegren 1991). We found higher risk
for DVCs during winter compared to the other seasons. At
northern latitudes, light conditions vary substantially
between seasons. During autumn and winter, we often
find peaks in activity pattern of deer during dark periods after
dawn and before dusk (Godvik et al. 2009, Pepin et al. 2009).
During winter, this often corresponds with peaks in daily
traffic level. In northern summers, darkness is virtually
absent. Correspondingly, the proportion of meetings
between cars and deer that occur during darkness is much
higher during winter than other parts of year. In addition,
roads are more slippery during winter when snow is present
adding to the increased risk of DVCs during this time of
year. Peaks in collision rates are found in dark periods in
white-tailed deer (Haikonen and Summala 2001, Sudharsan
et al. 2006) and moose (Haikonen and Summala 2001,
Dussault et al. 2006, Danks and Porter 2010). Also, more
deer are present at low elevation valley bottoms with more
roads during winter because of seasonal migration (Mysterud
et al. 2011).
Pastures are important deer habitats that are selected
during all seasons (Godvik et al. 2009), and much of the road
crossings by red deer are probably motivated to get access to
pastures (Gagnon et al. 2007). Our study shows a higher risk
for DVCs close to pastures even with better visibility in this
open habitat type. Red deer likely take higher risks when
approaching pastures and cross roads close to pastures.
Proximity to pasture seems to be an important factor when
evaluating DVC risks (Tappe and Enderle 2007) and has to
be considered when planning mitigation measures (Gagnon
et al. 2007). We also found an increased risk for DVCs with
increasing cover of productive forest, whereas rugged terrain
was partly supported to increase risk of DVCs (terrain
ruggedness were included in the most supported model of
DVC vs. random points, but not in the model of DVC vs. red
deer crossing points). Both these factors probably affect the
ability for drivers to detect crossing red deer in time.
Accordingly, the risk of DVCs decreased with visibility in
white-tailed deer (Bashore et al. 1985, Nielsen et al. 2003),
7
Table 4. Parameter estimates and test statistics from the most parsimonious model (exponential resource selection function [RSF] model with maximum
likelihood estimates and nonparametric bootstrap standard errors [B ¼ 99]) risk model contrasting red deer–vehicle collision (DVC) sites (coded 1) with red
deer crossing sites (coded 0) in Møre & Romsdal and Sør-Trøndelag, Norway, 2006–2009. Estimates of factor variables are relative risk relative to a reference
category and estimates for numeric variables are the change in log odds per unit of the variable (1% for productive forest cover).
Season (spring vs. autumn)
Season (summer vs. autumn)
Season (winter vs. autumn)
Road category (medium vs. major)
Distance to pasture (>50 m vs. <50 m)
Productive forest cover (%)
Speed limit (60 vs. 50 km/hr)
Speed limit (80 vs. 50 km/hr)
Road category (medium) speed limit (60 km/hr)
Road category (medium) speed limit (80 km/hr)
Season (spring) distance to pasture (>50 m)
Season (summer) distance to pasture (>50 m)
Season (winter) distance to pasture (>50 m)
Estimate
SE
Z
Lower 95% CI
Upper 95% CI
0.552
0.062
1.579
1.259
0.885
0.025
1.918
2.665
0.126
3.223
0.759
1.227
0.617
0.177
0.245
0.166
0.419
0.220
0.003
0.385
0.360
0.607
0.463
0.291
0.559
0.304
3.123
0.251
9.510
3.002
4.020
8.040
4.979
7.404
0.208
6.963
2.610
2.196
2.028
0.898
0.419
1.254
2.081
1.317
0.019
1.163
1.959
1.063
4.130
0.189
2.323
0.021
0.205
0.542
1.905
0.437
0.454
0.031
2.673
3.370
1.316
2.315
1.329
0.132
1.213
moose (Seiler 2005), roe deer, and wild boar (Sus scrofa; Malo
et al. 2004). Therefore, landscape characteristics need to be
taken into account when assessing mitigation efforts.
Fencing, over- and underpasses, vegetation modification of
road edge, and traffic signs are the most common mitigation
measures to reduce DVCs (Langbein et al. 2011). Target
clearance along the roads has been increasing in recent years,
in particular to reduce the number of collisions of moose and
deer (Iuell et al. 2003). However, few studies have tested the
effect of such measures. Our study shows a positive effect of
road edge clearance, with a 53% reduction in DVCs during
winter season. Interestingly, we found no effect of road edge
clearance during summer. This was not caused by regrowth,
because new vegetation was removed every summer in the
treatment period. Two studies in Norway reported a positive
Winter (<50 m)
Relative risk of DVC
Parameter
Summer (<50 m)
1400
1400
1200
1200
1000
1000
800
800
600
600
400
400
200
200
0
0
0
15
30
45
60
75
90
0
60
75
90
1200
1200
1000
1000
800
800
600
600
400
400
200
200
0
15
30
45
60
75
90
0
15
30
45
60
75
90
Figure 3. Relative risk (SE) for red deer crossings ending in deer–vehicle
collisions (DVCs) as a function of productive forest cover (%) during
summer and winter seasons, at 2 distance categories form pastures (<50 m,
>50 m) and at speed limits of 50 km/hr (gray lines) and 80 km/hr (black
lines) in central Norway, 2006–2009. The predictions are based on the most
parsimonious model and values are shown for major roads.
100
4
50
0
60−70
80
Speed limit (km/hr)
Figure 2. Relative risk (SE) for red deer crossings ending in deer–vehicle
collisions (DVCs) as a function of speed limit (50, 60–70, and 80 km/hr)
during summer (gray) and winter (black) seasons at 2 distance categories
from pastures (<50 m: solid dots, >50 m: open dots) in central Norway,
2006–2009. The predictions are based on the most parsimonious model.
Predicted values are for major roads and for mean values of numeric
covariates (productive forest cover ¼ 20.9%).
Number of collisions / month
Relative risk of DVC
45
Productive forest cover (%)
150
8
30
Summer (>50 m)
1400
0
50
15
Winter (>50 m)
1400
0
Winter <50 m
Winter >50 m
Summer <50 m
Summer >50 m
80 km/hr
50 km/hr
Treatment road
Control road
4
3
3
2
2
1
1
0
0
Before
After
Winter
Before
After
Summer
Figure 4. Predicted number of red deer–vehicle collisions (DVCs) per month
(95% CI) at the treatment road (vegetation clearance; solid dots) and the
control road (open dots), before and after roadside vegetation clearance and
grouped into winter and summer seasons in central Norway, 2003–2010.
The Journal of Wildlife Management
9999
Table 5. Parameter estimates and test statistics for the number of red deer–vehicle collisions (DVC) per month as a function of the spatiotemporal effect of
vegetation removal along Highway 70 (treatment) and other covariates in central Norway, 2003–2010. Two different negative binomial generalized linear
models are fitted; first a full year model incorporating the effect of season, and second as a winter season only model.
Parameter
Full year model
Intercept
Area (treatment vs. control)
Road edge clearance (before vs. after)
Season (winter vs. summer)
Population density index
Area road edge clearance
Area season
Road edge clearance season
Area road edge clearance season
Winter model
Intercept
Area (treatment vs. control)
Road edge clearance (before vs. after)
Population density index
Monthly maximum snow depth
Area road edge clearance
Estimate
SE
Z
P-value
0.364
1.093
0.752
1.745
0.005
1.226
0.732
0.874
0.884
0.433
0.267
0.308
0.303
0.003
0.431
0.498
0.519
0.519
0.841
4.096
2.440
5.759
1.622
2.844
1.471
1.683
1.129
0.401
<0.001
0.015
<0.001
0.105
0.004
0.141
0.092
0.259
0.191
1.179
0.730
0.005
0.006
1.245
0.460
0.260
0.291
0.003
0.004
0.404
0.415
4.542
2.509
1.492
1.737
3.083
0.678
<0.001
0.012
0.136
0.082
0.002
effect of vegetation clearing along the railway for the number
of collisions of moose (Jaren et al. 1991, Andreassen
et al. 2005). After clearing the railway line, the number of
collisions decreased by approximately 50%. In Sweden, an
accident reduction of nearly 20% was found for moose–
vehicle collisions after clearing of bushes and branches below
3 m within 20 m from the road (Lavsund and
Sandegren 1991). Corresponding figures for the effects of
clearing along the road are sparse for both moose and red
deer in Scandinavia and elsewhere. The effect of road edge
clearance is likely at least partly a car driver effect; clearance
gives better visibility along roadsides and possibilities for
discovering deer earlier as they are approaching the road. We
show road edge clearance is an effective measure even if the
traffic level is quite low (700–2,040 cars/day). Even though
measures ideally should be conducted on large scale, our
study demonstrates that road clearance is efficient, and
probably more cost effective, when targeted at shorter road
stretches with particular high DVC frequencies.
MANAGEMENT IMPLICATIONS
Our study shows that speed limitation within risk areas for
DVCs can reduce collision risk substantially. Reducing speed
limits during either the entire winter season or when snow
conditions are severe may be an effective compromise if a
permanent reduction in speed is difficult to implement.
Targeting this measure within dense forest habitats with a
variable topography in the vicinity of pastures should be most
effective. Likewise, vegetation clearance could reduce DVCs,
especially during winter season, and should similarly be
targeted in dense forest habitats close to pastures because
they are the stretches with the highest risk of DVCs.
Effective clearance can thus be achieved if completed in late
fall when vegetation growth has ceased.
ACKNOWLEDGMENTS
This study was funded by the Research Council of Norway
(“Natur og næring”-program; project no. 179370/I10), the
Meisingset et al.
Collision Risk of Red Deer
Directorate for nature management and Norwegian
Institute for Agricultural and Environmental Research.
Marking was mainly funded by game fund of various
counties, municipalities, and red deer management units,
and we are grateful to all people who have assisted in the
marking of animals. Thanks to J. Cabell for carefully
improving the English writing. We are grateful to the
Associate Editor, and 2 anonymous referees for very
valuable comments that led to substantial improvements in
the manuscript. Authors have no conflict of interest related
to this work.
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SUPPORTING INFORMATION
Additional supporting information may be found in the
online version of this article at the publisher’s web-site.
The Journal of Wildlife Management
9999