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The presence of rabbits adjacent to road increases polecat road mortality

Authors:

Abstract

Road mortality is an increasing problem for terrestrial vertebrate conservation due to the increase of both road numbers and vehicle Xow. We hypothesize that the proba-bility of a predator being killed on the road is related to the presence of its prey adjacent to the road, which is likely to be related to the use that these predators make of road verges. We aim to identify the features of speciWc stretches of road where road-kills of a predator (European polecat) occur in Mediterranean landscapes, including the presence of its main prey (European rabbit) and landscape and road features. We compared 85 100 m long stretches of road where at least one road-kill was recorded with 104 stretches without any road-kill in a dry agricultural landscape in central Spain. We used regression analysis to investigate the relationship between road-kill occurrence and the features in the 67% of the cases. Road-kill stretches were characterised by greater numbers of rabbit burrows in the road verges and by higher traYc Xow and speed (i.e. higher speed limit, lower proportion of heavy vehicles, wider road and lower proportion of unbroken central lines). Road-kill stretches also had more metres built over bridges and lower densities of people. We validated our best model with a dataset (the 33% of the cases) not included in its develop-ment, which correctly classiWed 82% of road-kill stretches and 89% of non-road kill stretches. Our results highlight the need for taking into account food resource distribution when studying causes of animal road-kills.
Biodivers Conserv (2009) 18:405–418
DOI 10.1007/s10531-008-9499-9
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ORIGINAL PAPER
The presence of rabbits adjacent to roads increases
polecat road mortality
R. Barrientos · L. Bolonio
Received: 17 December 2007 / Accepted: 26 September 2008 / Published online: 15 October 2008
© Springer Science+Business Media B.V. 2008
Abstract Road mortality is an increasing problem for terrestrial vertebrate conservation
due to the increase of both road numbers and vehicle Xow. We hypothesize that the proba-
bility of a predator being killed on the road is related to the presence of its prey adjacent to
the road, which is likely to be related to the use that these predators make of road verges.
We aim to identify the features of speciWc stretches of road where road-kills of a predator
(European polecat) occur in Mediterranean landscapes, including the presence of its main
prey (European rabbit) and landscape and road features. We compared 85 100 m long
stretches of road where at least one road-kill was recorded with 104 stretches without any
road-kill in a dry agricultural landscape in central Spain. We used regression analysis to
investigate the relationship between road-kill occurrence and the features in the 67% of the
cases. Road-kill stretches were characterised by greater numbers of rabbit burrows in the
road verges and by higher traYc Xow and speed (i.e. higher speed limit, lower proportion of
heavy vehicles, wider road and lower proportion of unbroken central lines). Road-kill
stretches also had more metres built over bridges and lower densities of people. We
validated our best model with a dataset (the 33% of the cases) not included in its develop-
ment, which correctly classiWed 82% of road-kill stretches and 89% of non-road kill
stretches. Our results highlight the need for taking into account food resource distribution
when studying causes of animal road-kills.
R. Barrientos (&)
Departamento de Ecología Funcional y Evolutiva, Estación Experimental de Zonas Áridas,
CSIC, General Segura 1, 04001 Almeria, Spain
e-mail: barrientos@eeza.csic.es
L. Bolonio
Grupo Ornitológico Alcedo, Facultad de Biología, Universidad de Alcalá de Henares,
N-II, km. 36.5, 28871 Alcala de Henares, Madrid, Spain
e-mail: luis.bolonio@terra.es
406 Biodivers Conserv (2009) 18:405–418
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Keywords Carnivore conservation · Mustela putorius · Oryctolagus cuniculus ·
Resources distribution · Road verge · TraYc · Unbroken line
Abbreviations
AIC Akaike information criterion
AICc Corrected Akaike information criterion
IGN The Spanish national geographic institute
PC Principal component
PCA Principal component analysis
UTM Universal transverse mercator coordinate system
Introduction
Carnivores are commonly identiWed as species whose conservation is more diYcult to
guarantee due to their highly demanding habitat requirements (e.g. Ginsberg 2001).
Present-day conservation strategies for carnivores focus on the integration of these
species into multi-use landscapes dominated by people (Linnell et al. 2000; Iuell et al.
2003). Roads cause one of the most important anthropogenic impacts on wildlife
communities and, as the cause of direct mortality, may reduce dispersal and reproduc-
tive or colonization events (reviewed in Forman and Alexander 1998; Spellerberg 1998;
Trombulak and Frissell 2000; Forman et al. 2003). Even without road mortality, roads
can cause barrier eVects. For example in a study with another mustelid species, the
badger (Meles meles), Clarke et al. (1998) found that high traYc loads may discourage
badgers from attempting to cross major roads. Similar Wndings have also been found for
other species (reviewed in Forman and Alexander 1998; Spellerberg 1998; Trombulak
and Frissell 2000; Forman et al. 2003). However, roads can increase the foraging
opportunities for predators through the availability of road-kill carrion (reviewed in
Little et al. 2002). We hypothesize that prey availability in road verges can increase the
fatality risk for predators. We tested this hypothesis with a study of the European
polecat (Mustela putorius L.) and its main prey in several ecosystems, the European
rabbit (Oryctolagus cuniculus L.).
The polecat is a small carnivore widely distributed in Europe where it remains in low
densities throughout most of its range, particularly in the Mediterranean peninsulas
(Mitchell-Jones et al. 1999; Marcelli et al. 2003; Virgós 2003; Virgós et al. 2007b). For
several decades, the species has undergone a serious decline in population levels through-
out the majority of its distribution. This decline has been caused by a combination of
factors, some human related (i.e. hunting, road-kills), but competition with alien species,
hybridization processes or low eVective population sizes have also been implicated (Blandford
1987; Davison et al. 1999; Mitchell-Jones et al. 1999; Sidorovich 2000; Lodé et al. 2003).
Recent status assessments in some parts of Europe have suggested that the polecat has
declined or is at unfavourable conservation status (see Virgós et al. 2007b for the Iberian
Peninsula). Consequently, it is currently listed on Annexe V of the EC Habitats and Species
Directive and Appendix III of the Bern Convention. In Spain, the polecat was recently
listed as “almost threatened” (Virgós et al. 2007b).
It has been suggested that uncultivated landscapes are important habitat resources for
carnivores in the inhospitable agricultural ecosystems as they provide protection and
connectivity among suitable habitats (Virgós 2001). The pattern of use of road verges by
polecats is unclear as Blandford (1987) suggests that polecats make frequent use of this
Biodivers Conserv (2009) 18:405–418 407
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habitat, whereas Rondinini et al. (2006), contrary to their own prediction, found that road
verges were used by polecats in proportion to their availability. Habitat use by polecats is
dependent on habitat structure and food dispersion, visiting prey-rich habitat patches most
frequently (Lodé 1994; Baghli et al. 2005; Zabala et al. 2005; Rondinini et al. 2006; Mestre
et al. 2007). In agricultural landscapes, roadside vegetation is commonly more developed
than surrounding Welds as vegetation growth is promoted by water running oV the road into
the ditch (review in Forman et al. 2003). In addition, roadside vegetation is rarely harvested
(Bellamy et al. 2000). Thus, if we assume that vegetation requirements of polecats could be
met by roadside strips, the probability of road-kill, mediated by the use of roadsides by
polecats, may depend on the presence of their prey. Rabbits, which commonly place their
warrens in road ditches (authors, personal observation), are the main prey for polecats in
several habitats, especially in Mediterranean ones (Roger 1991; Lodé 1997; Schröpfer et al.
2000).
Road mortality has been implicated as one of the major factors preventing the mainte-
nance of healthy and stable populations of polecats in Mediterranean landscapes (Virgós
et al. 2007b). In other geographic regions, where the ecology of the polecat is better
known, road fatalities are also a major cause of polecat deaths (Blandford 1987; Birks
1997; Mitchell-Jones et al. 1999; Lodé 2003; Kristiansen et al. 2007). Despite these
Wndings and that road-kills seem to be concentrated along certain stretches of road
(Blandford 1987), no studies have analysed habitat and road-related variables at speciWc
locations where road-kills occur. Road mortality patterns in small animals seem to be
species-speciWc (Clevenger et al. 2003). However there is a general paucity of knowl-
edge regarding the relationship between road features and small-animal fatalities
(Ginsberg 2001). TraYc characteristics and road features play an important role in
road-kills. For instance, the probability of road-kill mortality increases with both traYc
volume and speed (Van Langevelde and Jaarsma 2004). However, some road eVects such
as barrier eVects can adjust the level of mortality. For example Clarke et al. (1998) found
that roads with high traYc loads have proportionately fewer badger road-kills due
increased avoidance of these roads by the badgers. Unfortunately, this complex relation-
ship is hard to capture without radio-tracking studies. Road width is another important
feature, animals needing more time to cross wider roads, leading to a decrease in the
probability of a successful crossing (Van Langevelde and Jaarsma 2004). This is particu-
larly important for small animals (review in Forman et al. 2003). However, few of the
studies that have focused on small vertebrates have analysed road-kill in detail at speciWc
points where road-kills have occurred in order to develop predictive models of fatalities
(but see Saeki and Macdonald 2004; Ramp et al. 2005, 2006).
The aim of our study was to identify the features of speciWc stretches where polecat
road-kills occur and to provide the basis of a strategy to prevent this mortality. SpeciWcally,
we focused our research on three basic characteristics: (1) The presence of prey near the
road. In our study area, rabbits make up the bulk (mean 43.6% year-round) of polecat diet
by biomass (Cuesta 1994). Consequently, we expected road-kills to be associated with
those stretches of road where rabbits were more abundant. (2) Roadside vegetation.
Polecats are expected to make greater use of vegetated patches (Lodé 1994; Baghli et al.
2005; Zabala et al. 2005; Rondinini et al. 2006) and consequently the number of road-kills
is expected to be positively associated with greater amounts of vegetation. (3) Road and
traYc characteristics. We expected that stretches of road with higher speed limits and traYc
volume would be linked with larger numbers of polecat road-kills (Van Langevelde and
Jaarsma 2004).
408 Biodivers Conserv (2009) 18:405–418
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Materials and methods
Study area and road kill survey
This study was carried out in the Tajo valley, Toledo province, central Spain, from August
2002 to July 2004. The Tajo basin ranges between 350 and 850 m in altitude. The climate is
Mediterranean and during the study period annual rainfall averaged 340 mm. The average
daily maximum temperature was 27.1°C (August) and the minimum 3.6°C (December).
The dominant land cover is dry crops such as cereals, fallow and olive Welds (altogether dry
crops cover the 54.6%). The remaining land cover consists of irrigated crops (mainly maize
Welds) (2.1%), urban areas (10.1%) and non-cultivated land (33.2%) (dominated by scrub-
land composed of Retama sphaerocarpa and Stipa tenacissima). The study area was
bounded to the North by motorway N–V (40°02N, 04°26W), by the city of Toledo
(39°51N, 04°01W) to the East, by the Montes de Toledo mountain range (39°35N,
04°37W) to the South and by the roads CM-4015 and CM-4102 (39°48N, 04°38W) to
the West. The extent of road development and traYc Xow in the study area can be consid-
ered to be representative of that found in most of central Spain.
We surveyed polecat road-kills on all of the two-lane paved roads in the study area using
a car. These roads total 246 km divided into two routes of 121 and 105 km. The number of
surveys conducted was 41; two surveys per month except for 7 months when we could only
carry out one survey. Thus, the total distance covered was greater than 10,000 km.
The survey was conducted by two observers. Whilst one drove at 40–50 km/h, the other
looked for carcasses on the road surface. Polecats were often found in the middle of the
road, although we cannot discount that some of the animals killed in the centre of the road
were moved to the ditch at the side by motorists or just by the crash. To assess the degree to
which we overlooked carcasses in the ditches, we randomly surveyed over 100 km of
verges on foot, along all the road classes. We found only two polecat carcasses not detected
by the previous vehicle survey. Despite surveys done by foot being more reliable, surveys
were conducted by car as much larger distances could be covered. Furthermore, the proba-
bility of missing carcases in our car surveys was likely to be constant across the study area.
All the carcases were removed from the road surface in order to avoid double counts.
Undercounting of carcases due to removal by scavengers was likely to be negligible as the
only scavenger common in the area is the magpie (Pica pica) which is not able to remove
polecat carcasses. As such, we could monitor for months some carcasses at identiWable
locations.
We divided the two sampling routes into 100 m stretches each delimited by two hecto-
metre posts. To select random stretches for their inclusion in the dataset, we chose at
random one stretch in every 20, discarding those with road-kills. Thus, if no polecat was
found during any survey in a selected stretch, this was considered to be a “non-road-kill
stretch” and it was included in the dataset. The stretches where we found any road-kill were
considered “road-kill stretches”. Although sampling was not paired, non-road-kill stretches
were distributed along the study area in proportion to the total length of every road type.
Overall, we randomly chose 104 non-road-kill stretches in order to compare their charac-
teristics with those of the 85 stretches where at least one road-kill was recorded. Before
constructed the model, we randomly divided the whole dataset into two, one made up of
126 cases (57 road-kill stretches and 69 without them) to use to develop the models. The
second dataset was composed of 63 cases (28 road-kill stretches and 35 without them) and
was used to validate the models.
Biodivers Conserv (2009) 18:405–418 409
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Variables associated with road-kills
Landscape variables were assessed within a circular area based on the central point of the
road section and extending 50 m along roadside on either side of the road. This scale was
chosen as we started on the premise that rabbit presence is a key factor determining the use
of road verges by polecats and therefore it will also aVect the probability of polecats being
killed on the road. The chosen scale for landscape variables matches the size of rabbit home
ranges in Mediterranean habitats, which is about one hectare (Lombardi et al. 2007). The
presence of fresh rabbit carcases can be another important factor attracting polecats on to
roads (Birks and Kitchener 1999). As such, it is worth mentioning that rabbit carrion
reached up to 1.2 carcases/km and survey on some stretches during our study (Authors,
unpublished data). It is worth mentioning that the landscape composition is quite homoge-
neous at small scales as the area is dominated by extensive agriculture and non-cultivated
lands and that no changes in agricultural use were detected while carrying out the study.
This size of buVer zone also matched the 100 m measurement unit used for the road-related
variables. Data obtained from a stretch of road where road works occurred during the study
were excluded from the datasets. Following similar criteria to Ramp et al. (2005), variables
were selected for inclusion in the models when they were known from literature to be
related to animal fatalities, to be resources used by polecats or when they were manage-
ment variables not speciWc to our study area (see Table 1 for variables studied and their
deWnitions). Thus, variables such as dry Welds, non-cultivated land or other land use types
were incorporated in order to investigate the potential inXuence of landscape on polecat
mortality through variable resource distribution eVects (e.g. foraging, protection). The
number of rabbit burrows and the distance from the road to the nearest burrow were used as
measurements of the local abundance of rabbits. The study area is mainly deforested, prob-
ably leading to polecats to concentrate their movements in those patches with greater
amounts of vegetation, especially riverbanks (Zabala et al. 2005; Rondinini et al. 2006;
Mestre et al. 2007). To test for such a vegetation eVect on road-kills, we included the
percentage of woody vegetation cover and the distance to the nearest stream as model
variables. The distance to the nearest house and town were used as proxies for human
encroachment (i.e. greater presence of humans in the Weld, intensiWcation of agricultural
practices, alteration of ecosystems, pollution, etc.). Vehicle speed and traYc Xow rate can
aVect the likelihood of collisions with animals (Malo et al. 2004; Van Langevelde and
Jaarsma 2004; Seiler 2005), and we therefore included the speed limit, percentage of heavy
vehicles, road width, percentage of unbroken line (where overtaking is forbidden) and
traYc intensity as model variables. The lengths of sections of road with embankments,
going over bridges and the distance to the nearest non-wildlife underpass (mainly culverts)
were also included to control for the potential inXuence of structures which may funnel or
facilitate animal movements at particular crossing points (Yanes et al. 1995; Malo et al.
2004). Finally, we included road class as a categorical factor, encompassing a range of road
traits. This categorical variable consisted of Wrst or second class roads, based on the auton-
omous govern classiWcation of the two-lane paved roads. The characteristics of Wrst class
roads, measured in 63 random 100 m long stretches, were (mean §SE): 10.2 §0.1 m for
road width, 43.2 §4.6% unbroken line and a road surface of generally good quality. In
contrast, second class roads, measured in 41 random stretches, were narrower (6.5 §
0.2 m), had more stretches with bends (i.e. higher percent of unbroken line: 49.6 §6.6%)
and had road surfaces of lower quality. We initially included variables that were likely to
be correlated as an attempt to incorporate the best set of predictors in the models. However,
410 Biodivers Conserv (2009) 18:405–418
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where correlation between variables was found we excluded the one with the least explana-
tory power (see “Statistical analyses”).
Altogether 14 quantitative local-scale variables (six landscape and eight road-related; see
Table 1 for their measurement units) and one categorical variable were used. The quantitative
variables were counted, measured or scored visually (percentage of vegetation coverage) and
always by the same researcher (R.B.). TraYc data were obtained from Junta de Comunidades
de Castilla-La Mancha and Diputación de Toledo. We used the monthly mean for traYc
variables. Finally, the three landscape large-scale variables were measured using a 1:50,000
topographic map of Spain, IGN, using the coordinates Universal Transverse Mercator coordi-
nate system (UTM) of the mid-point of a stretch as its location.
Statistical analyses
Quantitative diVerences between stretches with and without road-kills were evaluated using
unpaired t-tests (e.g. Seiler 2005) and Bonferroni step-down correction (Holm 1979). Tests
were calculated using STATISTICA 6.0 (Statsoft 2003).
We used a Factor Analysis with Principal Component Analysis (PCA) and the Varimax
normalized factor rotation to build a correlation matrix to explore the degree of association
among variables. For the subsequent model development, and in order to minimize multi-
collinearity (correlations >0.6) among independent variables, we only included the most
highly correlated variable with each of the PC factors, discarding the remaining variables.
Due to the strong associations of the variables selected according to PCA factor loadings,
seven variables were removed before the model process: NON CULT, OTHER, WOODY,
N_BURROWS, HEAVY, WIDTH and PASS (Appendix 1). We included road type
Table 1 The variables measured in each 100 m stretch used to study the factors that diVerentiate stretches
with polecat road-kills from those without them
Variable DeWnition
Landscape local-scale variables
DRY % Cover of dry crops within a radius of 50 m around both sides of the road
NON_CULTIV % Cover of non cultivated lands within a radius of 50 m around both sides of the road
OTHER % Cover of other land uses within a radius of 50 m around both sides of the road
WOODY % Cover of woody plants in within a radius of 50 m around both sides of the road
M_BURROW Distance (m) to the nearest rabbit burrow on any side of the road
N_BURROWS Number of rabbit burrows within a radius of 50 m around both sides of the road
Landscape large-scale variables
TOWN Distance (m) to the nearest town
HOUSE Distance (m) to the nearest isolated house on any side of the road
RIVER Distance (m) to the nearest river
Road-related local-scale variables
SPEED Speed limit on the stretch of road
TRAFFIC Monthly mean number of vehicles per day along the stretch
HEAVY Mean % of heavy vehicles per day along the stretch
WIDTH Road width (m)
PASS Distance (m) to the nearest non-wildlife underpass
EMBANKMENT Total metres of embankments of > 1 m height and > 60° slope on both sides
of the 100 m stretch
BRIDGE Length (m) of the 100 m stretch built over bridges.
UNBROK_LINE % of the length of the 100 m stretch of the road where overtaking is forbidden.
CLASS Categorical variable: 1 Wrst, 0 second class regional road
Biodivers Conserv (2009) 18:405–418 411
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(i.e. Wrst or second class) as categorical factor in our models. Before starting the modelling
process, we randomly set aside 33% of the data (i.e. 63 cases, of which 28 were road-kill
stretches and 35 were without road-kills) to use them for model validation. Therefore
model development used 67% of the data (i.e. 126 cases, of which 57 were road-kill
stretches and 69 were without road-kills). We conducted a regression analysis utilising a
binomial distribution (road-kill versus non-road-kill stretches) and logit link function. We
used the best subsets procedure and the Akaike’s information criterion (AIC) to identify the
set of models best explaining the occurrence of road-kills in the 126 cases. We used this
technique because it yields consistent results independent of the order in which variables
are included in the model and allows models with diVerent numbers of parameters to be
directly compared with each other (Burnham and Anderson 2002). We used AIC values
corrected for small sample size (i.e. AICc) as the ratio between the number of observations
and estimator variables was under 40 (Burnham and Anderson 2002). The models were
developed using STATISTICA 6.0 and the AIC values were corrected with Microsoft
EXCEL 2003 (Microsoft) following Burnham and Anderson (2002). The models with the
lowest AICc represent the best compromise between a maximal Wt and a minimal number
of explanatory variables (i.e. statistical parsimony). To evaluate the relative explanatory
power of competing best models, we calculated the Akaike weights (i). The evidence
ratio was calculated to compare the Akaike weights of the best model and competing ones
to determine to what extent one was better than another (Burnham and Anderson 2002). In
order to estimate the relative importance of every variable included in any of the best mod-
els we calculated the sum of Akaike weights of the models where these variables were
included (Burnham and Anderson 2002). The signiWcance of variables included in the best
model was assessed using the log-likelihood ratio test because it is the most suitable for
small sample sizes (Moya-Laraño and Wise 2007). Model validation using data not used in
the model development was carried out using simple logistic regression (e.g. Seiler 2005).
Results
Road-kill pattern
A total of 107 polecat road-kills were recorded in 85 stretches (2 stretches with 3 casualties,
18 with 2 and 65 with only 1) over a period of 24 months. DiVerences between road-kill
and non-road-kill stretches based on t-tests are shown in Table 2.
Road-kill stretches had signiWcantly more rabbit burrows being the nearest one at
shorter distance. These stretches also had higher speed limits and traYc intensities, but had
lower frequencies of heavy vehicles. Road sections with fatalities were signiWcantly wider
and had lower percentages of unbroken line (i.e. were straight). Road-kill stretches were
also closer to streams and had lower percentages of other land uses (e.g. urban, irrigated
crops).
The regression procedure with the best subset analysis provided a set of 13 models
which could be considered as plausible models according to their AICc (i.e. the diVerence
between their AICc and the lowest one was less than two; Burnham and Anderson 2002).
The six best models are shown in Table 3. Our results do not give clear support for a single
model amongst these six, indicated by the small diVerences in AICc values and the compa-
rable values of Akaike weights (Table 3). Importantly, however, all the models included
M_BURROW, BRIDGE, SPEED and UNBROK_LINE (i.e. i= 0.5994). Less inXuen-
tial were TRAFFIC (i= 0.05262), HOUSE (i= 0.5174), TOWN (i= 0.2994) and
412 Biodivers Conserv (2009) 18:405–418
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RIVER (i= 0.0832). We selected model no.1 (Table 4) as the best one because it repre-
sents the best compromise between a maximal Wt and a minimal number of explanatory
variables. The log-likelihood ratio test suggests that the most important variable associated
with the occurrence of polecat road-kills was the presence of rabbit burrows close to the
roadside.
Table 2 The results for the unpaired t-tests (mean §SD) comparing the 17 variables quantifying habitat and
road-related features between road-kill and non-road-kill stretches
The table shows the P-values with Bonferroni step-down correction (Holm 1979). See Table 1 for variable
codes
Variable Non-road-kill stretches Road-kill stretches Unpaired t-test
Mean §SD Mean §SD t-value P-value
DRY 49.14 §3.69 50.97 §3.75 0.35 0.7305
NON_CULT 32.12 §3.24 42.39 §3.49 1.94 0.0535
OTHER 18.75 §3.27 6.71 §1.80 ¡3.06 0.0275
WOODY 10.54 §1.54 11.45 §2.24 0.34 0.7315
M_BURROW 22.07 §0.75 9.53 §0.92 ¡10.68 <0.0001
N_BURROWS 3.02 §0.69 27.69 §2.74 9.54 <0.0001
TOWN 2475.96 §205.72 2518.82 §158.18 0.16 0.8735
HOUSE 272.12 §27.38 315.88 §31.27 1.06 0.2921
RIVER 437.50 §45.02 284.70 §39.15 ¡2.50 0.0133
SPEED 92.98 §12.53 99.53 §2.13 4.76 <0.0001
TRAFFIC 2445.66 §204.05 3847.08 §264.74 4.26 <0.0001
HEAVY 13.76 §0.81 9.46 §0.44 ¡4.34 <0.0001
WIDTH 8.75 §0.20 10.17 §0.10 6.09 <0.0001
PASS 91.40 §2.51 84.40 §3.36 ¡1.70 0.0902
EMBANKMENT 10.77 §3.46 20.12 §5.33 1.52 0.1301
BRIDGE 0.64 §0.35 0.88 §0.50 0.42 0.1301
UNBROK_LINE 45.72 §3.80 30.59 §3.87 ¡2.77 0.0062
Table 3 The set of six models that best separated stretches with polecat road-kills from those without them
The AICc is the diVerence in AICc values compared to the estimated best model (lowest AICc) what allows
the ranking of models from an estimated best (top of the table) to the worst. AICc weight is the estimated
probability t hat a model is the best model in the set. Evidence ratio indicates to what extent one model is bette
r
than another
Model no. Variables contained in the model KAICc
(i)
AICc
weight (i)
Evidence
ratio
1 M_BURROW + HOUSE + TRAFFIC + BRIDGE
+ UNBROK_LINE + SPEED
6 0 0.1461 0.00
2 M_BURROW + TOWN + HOUSE + TRAFFIC
+ BRIDGE + UNBROK_LINE + SPEED
7 0.0418 0.1430 2.11
3 M_BURROW + TRAFFIC + BRIDGE
+ UNBROK_LINE + SPEED
5 1.1552 0.0820 78.18
4 M_BURROW + TOWN + HOUSE + RIVER
+ TRAFFIC + BRIDGE + UNBROK_LINE
+ SPEED
8 1.2021 0.0832 82.40
5 M_BURROW + TOWN + HOUSE + BRIDGE
+ UNBROK_LINE + SPEED
6 1.3809 0.0732 99.47
6 M_BURROW + HOUSE + RIVER + TRAFFIC
+ BRIDGE + UNBROK_LINE + SPEED
7 1.4174 0.0719 103.13
Biodivers Conserv (2009) 18:405–418 413
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Model validation
Simple logistic regression analysis was used to validate the predictive capacity of our best
model (Table 4). The use of the independent dataset showed that the model correctly
predicted 82.1% of road-kill stretches and 88.6% of non-road-kill ones. Thus, our model
succeeded in distinguishing between the occurrence and non-occurrence of polecat road-kills.
Discussion
Our results highlight the need for taking into account the distribution of food resources
when studying animal road-kills. The presence or abundance of resources near roads may
determine the use that animals make of roadsides and this, in turn, may aVect their road-kill
rates. In our polecat–rabbit system, we obtained a model where the main feature of
stretches where road-kills were recorded was the presence of the polecat’s main prey next
to the road, measured as the distance of the nearest rabbit burrow to the roadside. Higher
vehicle speeds (i.e. higher speed limits and smaller percentages of unbroken line) and
higher traYc Xows were also associated with road-kill stretches. Road-kill stretches also
had greater lengths over bridges and were farther from the nearest house. Our best model
had a high success rate in predicting polecat road-kill stretches using the independent data-
set (82–89% of cases correctly classiWed).
Rabbits make up the bulk of polecat diet in many, mainly Mediterranean, regions
(reviewed in Lodé 1997; see also Birks and Kitchener 1999; Virgós 2002). These lagomorphs
are mainly hunted by polecats in their warrens (Cuesta 1994), although they can be consumed
as carrion as well (Birks and Kitchener 1999). Thus, a signiWcant cause of polecat road-kills
must be their hunting movements among rabbit warrens on roadsides, although other causes
not identiWed here (seeking mates, dispersal, territory marking, etc.) could also be important
sources of polecat casualties (e.g., see Sleeman 1988 for a study on stoats). There are three
potential reasons for rabbits to establish warrens along roadsides: (1) Both in cereal crops and
sandy uncultivated areas, rabbits build their warrens between tree and shrub roots, seeking
protection against warren collapse by using the roots as supporting structures (Palomares
2003; Gea-Izquierdo et al. 2005). However, human activities have eliminated roots from
Welds for agricultural reasons and other warren supporting/protecting structures, such as gran-
ite rocks or walls, are scarce in the study area (Gea-Izquierdo et al. 2005). Building warrens
inside road embankments could be an adaptation to avoid their warrens collapsing by using
the embankments as supporting structures. (2) Ploughing destroys rabbit warrens (Calvete
et al. 2004). Consequently, rabbits avoid cultivated crops as areas to place their warrens and
Table 4 The best model after regression analysis with best subset procedure following AICc criterion
including the variables that best separated road-kill stretches from non-road-kill ones
Parameter Estimate SE Log likelihood-2P
CONSTANT ¡9.787977 7.109625
M_BURROW ¡0.144964 0.028240 35.272 <0.001
HOUSE 0.001590 0.000920 3.155 0.076
TRAFFIC 0.000257 0.000122 4.926 0.026
BRIDGE 0.410774 0.235557 3.899 0.048
UNBROK_LINE ¡0.018162 0.007623 6.305 0.012
SPEED 0.115488 0.072240 5.346 0.021
414 Biodivers Conserv (2009) 18:405–418
1 C
positively select non-ploughed ecotones (e.g. road verges) (Calvete et al. 2004; Gea-Izquierdo
et al. 2005). (3) The rabbits are important prey for almost 30 predator species in the Iberian
Peninsula but the activity of several predators decreases near roads, particularly in cultivated
landscapes (Virgós 2001; Bautista et al. 2004), suggesting that road verges could act as safer
breeding zones for rabbits. This eVect could be reinforced by the absence of shooting near
roads, which is forbidden by law.
Although scarce, stretches of road built over bridges were prone to be black spots.
There are three potential explanations that we suggest for this: (1) streams are important
ecotones selected by rabbits to place their warrens (Calvete et al. 2004) and stretches of
road built over bridges could be visited preferentially by polecats during hunting (Zabala
et al. 2005). (2) Although it was discarded in the pre-modelling process (see Appendix
1), woody coverage was positively related to the lengths of sections over bridges,
probably because the occurrence of riparian woodlands is a dominant component of
vegetation coverage in agricultural deforested areas. Utilisation of riparian vegetation is
an essential part of polecat movements in the agricultural matrix due to the protection it
provides (Zabala et al. 2005; Rondinini et al. 2006; Mestre et al. 2007). (3) Once a
polecat is trapped on a stretch of road that is diYcult to exit (e.g. on a bridge), the animal
will move in an erratic way along the section dramatically increasing its chances of
being run over.
The role of diVerent habitat types in species survival in relation to landscape structure
(suitable amounts of habitat, connectivity among patches or the geometry of patches) is a
topic under continuous debate (Andrén 1994; Gutzwiller 2002). In agricultural landscapes,
where most of the natural habitat has been transformed, the residual semi-natural
vegetation along rivers or road verges is a key tool in the conservation biology of many
species, including carnivores (Virgós 2001). If this kind of habitat is large enough, the
eVect of habitat loss or fragmentation can be minimized (Andrén 1994; Gutzwiller 2002).
The non-agricultural habitat patches distributed across the agricultural matrix is capable of
maintaining high levels of biodiversity because it can provide abundant food through the
edge eVect, allows animals to forage close to vegetation cover safe from humans or preda-
tors (Macdonald 1995) and maintains the connectivity among suitable patches (Andrén
1994; Gutzwiller 2002). In fact, some prey species inhabiting agricultural landscapes are
settling in non-arable patches, such as road verges (e.g. Bellamy et al. 2000). Thus, the
location of prey may become more predictable, and these patches will be visited more
frequently by predators, leading to a greater number of road-kills of the predator as seems
to be the case in our polecat–rabbit system.
Road-kills occur along those stretches of road that support higher traYc densities and
where vehicles go faster as predicted the traYc Xow theory (Van Langevelde and Jaarsma
2004). Road-kills are predicted in our study by higher speed limits, a lower density of
heavy vehicles (i.e. allowing faster driving), wider roads and a lower amount of unbroken
line (i.e. straight stretches). An animal crossing is successful if an acceptable gap in the
traYc Xow appears at the start of the crossing (Van Langevelde and Jaarsma 2004). TraYc
speed determines traYc mortality because lower vehicle speeds increase the probability of
a successful road crossing as they provide both the driver and the animal with a greater
amount of time to react and avoid road-kill (Van Langevelde and Jaarsma 2004). The pole-
cat is a small mammal. About 1 kg in weight and a body length of 50 cm (Blandford 1987).
This is the reason why it is rarely included in the Weld-of-view whilst driving at high speed.
Furthermore, polecats are often crepuscular or nocturnal (e.g. Marcelli et al. 2003), so are
active on roads when visibility for drivers is lower. Finally, the particular morphology of
polecats, with short legs and a long body (Blandford 1987), likely adaptations to hunting
Biodivers Conserv (2009) 18:405–418 415
1 C
rabbits in their warrens (Cuesta 1994), means that this species requires more time to cross a
road, decreasing its probability of a successful crossing, particularly on wider stretches
(Van Langevelde and Jaarsma 2004).
Finally, the least signiWcant predictor in the best model was the distance to the nearest
isolated house, this being greater for the road-kill stretches The distance to the nearest town
was also included in some of the best models, with a similar pattern. We interpret these
results as human encroachment leading to a decrease in suitable habitat for polecats (Zabala
et al. 2005).
One weakness of our model was that we did not know the density of live polecats in the
areas studied and therefore could not quantify the eVect of this variable on the incidence of
road-kills. Several studies have found a positive relationship between population density
and road-kills (e.g. Gehrt 2002; Seiler 2005; but see Klocker et al. 2006). However, our
model correctly predicted 82% of road-kill stretches which suggests either that the road-kill
pattern is density independent or that some of the variables that we measured were
correlated with live polecat density, and thus we estimated polecat density indirectly. For
example, it might be expected that polecat densities are higher in areas where its main prey,
the rabbit, is more abundant. To estimate densities of live animals requires costly monitor-
ing eVort and would therefore not be feasible in most cases. The variables we measured are
much easier and cheaper to obtain and the model developed was successful, and we there-
fore suggest that this model is a valuable tool for predicting polecat road-kills.
Management implications
The management measures we suggest must be easy and cheap to carry out as the road-kill
stretches in this study represented only 3.8% of the total road network. First, the number of
road-kills could be signiWcantly reduced along stretches of road with high numbers of
warrens in road embankments. This could be done by fencing oV the road and connecting
both sides with underpasses to permit the normal transit of both rabbits and their predators.
Second, the establishment of rabbit warrens along the roadside could be managed. ArtiWcial
refuges are currently being used to reintroduce or maintain rabbit populations elsewhere
(e.g. Gea-Izquierdo et al. 2005) as rabbits are declining in several areas (Virgós et al.
2007a). These artiWcial warrens could be used as part of a management strategy to dissuade
rabbits from settling in road embankments, by placing the artiWcial warrens away from the
roads. Even simple earth mounds can be used as a cheaper alternative to building artiWcial
warrens, as they are commonly used by rabbits to build their own warrens (authors,
personal observation). Third, traYc speed could be reduced along stretches of roads with
high numbers of road-kills by the use of lower speed limits combined with the laying of
speed bumps. This would be particularly necessary on straight stretches of road with high
roadside rabbit warren densities. Lower vehicle speeds will certainly increase the success-
ful crossing rate for this mustelid (Van Langevelde and Jaarsma 2004).
Acknowledgements We are very grateful to I. Cardiel, R. Jiménez, P.M. García, D. Martínez, O. Frías, J.A.
Calvo, J.A. Lemus for their help in the Weld. We thank F. Valera, R. Václav, E. Virgós, J. Moya-Laraño, G.
Hernández-Milián, J. Birks, D.P. Sleeman and an anonymous referee for their helpful suggestions. J. Moya-
Laraño, Mark T. Bulling and B. Nicholls checked the English. TraYc data were provided by M. Vidal (Junta de
Comunidades de Castilla-La Mancha), A. J. Cervantes and J. Cobas (Diputación de Toledo) and A. Oliver
(TELVENT). The Dirección General del Medio Natural of Toledo Province (Junta de Comunidades de Castilla-
La Mancha) partially supported our research by means of TO-01-03 and TO-01-04.
416 Biodivers Conserv (2009) 18:405–418
1 C
Appendix 1
The results of the principal component analysis run prior to the development of the models in order to inves-
tigate the multicollinearity among independent variables
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WOODY 0.077028 0.633344 0.101267 0.369508 0.224009 ¡0.143506
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N_BURROWS 0.147825 0.027444 ¡0.132319 0.182981 0.218188 0.786171
TOWN 0.263878 ¡0.006824 0.377190 0.529965 ¡0.054079 0.212921
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RIVER 0.126340 ¡0.286418 0.083116 0.025225 0.384410 ¡0.539122
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TRAFFIC ¡0.161921 0.115909 ¡0.762606 0.150385 0.265780 0.115479
HEAVY ¡0.085134 0.117805 0.647747 0.082747 ¡0.053125 ¡0.223346
WIDTH 0.431084 ¡0.088140 ¡0.617466 0.248435 ¡0.061471 0.129852
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BRIDGE 0.043028 0.775742 ¡0.042387 0.075463 0.153974 0.017040
UNBROK_LINE ¡0.443517 0.143221 0.132539 0.129783 0.563282 0.069696
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... Land cover is a common predictor for animal abundance and dispersal (e.g. Cagnacci et al. 2011;Linnell et al. 1998), and can influence where individuals cross the road, and potentially also the driver's visibility of an approaching animal (Meisingset et al. 2014), thus impacting where WVC occur (Barrientos and Bolonio 2009;Malo et al. 2004;Joyce and Mahoney 2001). We characterized land cover using maps from Land Cover Project 2018 (resolution: 100 × 100 m; European Environment Agency 2018). ...
... The risk of a WVC occurring can also be influenced by road-related predictors such as ADT, speed and road density (Kent et al. 2021;Barrientos and Bolonio 2009;Jaarsma et al. 2007; Table 1). In Germany, the road network is categorized into five categories according to ADT and speed limits (Straßenverkehrs-Ordnung §3 n.d.; Statistisches Landesamt Baden-Württemberg 2022). ...
... At lower road densities, the probability for an animal to have to cross a road is generally decreased as fewer roads transect their home ranges. Areas with higher road densities and higher ADT, may be avoided by roe deer (Benhaiem et al. 2008;Coulon et al. 2008;Kent et al. 2021), contributing to a barrier or filtering effect of the roads on the movement of roe deer in the landscape (Barrientos and Bolonio 2009;Grilo et al. 2015;Madsen et al. 2002). ...
Article
Full-text available
Context To investigate the major impact of roads on wildlife, most studies focus on hot-spots of wildlife-vehicle collisions (WVC) to identify areas in need of mitigation measures. However, on road stretches where the frequency of WVC is low, a question arises: is this because those locations are 'safe’ places for wildlife to cross the road with little risk of collisions; or is it because individuals avoid approaching and crossing the road in these locations? Objectives In this study, we addressed this gap by evaluating how roe deer crossings are related to WVC risk across the road network. Methods We used 56 076 WVC locations between 2013 and 2017 to predict the spatiotemporal risk zones in response to environmental, road-related and seasonal predictors using Species-Distribution Modelling (SDM). We compared the predictive WVC risk to the location of 20 744 road crossing by 46 GPS-collared roe deer individuals. Results We found that the risk of WVC with roe deer tends to be higher on federal roads that are present in a density of approximate 2.2 km/km² and surrounded by broad-leafed forests and demonstrate that SDMs can be a powerful tool to predict the risk of WVC across the road network. Roe deer crossed roads more frequently in high WVC risk areas. Temporally, the number of WVC changed throughout the year, which can be linked to roe deer movement patterns rather than landscape features. Within this study, we did not identify any road segments that were a complete barrier to roe deer movement. Conclusions The absence of complete barriers to roe deer movement detected in the present study, might be due to the low spatial variation of the landscape, coupled with the high individual variation in movement behaviour. By applying our approach at greater spatial scales and in other landscape contexts, future studies can continue to explore the potential barrier impacts of roads on landscape connectivity. Exploring the relationship between crossing activity and collision risk can improve one’s ability to correctly identify road stretches that require mitigation measures to improve connectivity versus reduce collisions.
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... Although many carnivores are reluctant to cross roads (Clarke et al. 1998;Philcox et al. 1999;Fahrig and Rytwinski 2009;Benítez-López et al. 2010), some species are attracted to them when searching for food resources easy to obtain such as carcasses, rubbish or abundant small prey living in roadsides Barrientos and Bolonio 2009;Mata et al. 2017). Since transport corridors can provide new suitable habitats (i.e., predator-free with abundant food) for small prey species such as rodents and lagomorphs, these can reach high densities along roadsides and provide a predictable source of food for opportunistic predators (Bellamy et al. 2000;Barrientos and Bolonio 2009;Ascensão et al. 2012;de Redon et al. 2015). ...
... Thus, we assessed the potential effects of food availability, as well as habitat and road typerelated factors separating polecat roadkill location from random ones in an area of central Spain where the species is well distributed. We expect (i) rabbit abundance to be higher in polecat roadkill sites (Barrientos and Bolonio 2009;Barrientos and Miranda 2012); (ii) coverage of natural vegetation remnants to be higher around roadkill points as they provide better habitat both for this species and its prey (Lodé 1993;Calvete et al. 2004;Gea-Izquierdo et al. 2005;Zabala et al. 2005); and, finally, (iii) road type influences roadkills as different road categories have different traits. Namely, rights-of-way (the space between the road shoulder and the fence) in motorways are wider, have homogeneous management along dozens of kilometres, and are usually fenced (Ascensão et al. 2012), which would favour their use as surrogate habitats both for polecats and their prey in agricultural-dominated landscapes, as they both easily trespass large mammals-oriented road fences (Plante et al. 2019). ...
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Roads threaten the conservation of many wildlife species. Carnivores are one of the most susceptible groups due to their habitat requirements. We explored the roadkill patterns of European polecats (Mustela putorious) on motorways and roads to investigate if these patterns depend on road type, a research topic frequently neglected in the literature. We studied 85 roadkills on motorways and 73 on roads, and the corresponding number of random points with no roadkills in every road type. We characterized them with 7 habitat and 7 road-related variables. Roadkill sites were significantly associated with the abundance of rabbit burrows. However, this effect was stronger on motorways, as they provide more suitable habitat for the establishment of prey species on their wider rights-of-way, or on the road interchange islands, which provide wide unused spaces. In contrast, road interchange islands on conventional roads that are simple intersections and have narrower rights-of-way. Furthermore, roadkills occurred in areas with lower agricultural cover. Thus, natural habitats on roadsides could act as alternative foraging areas for this carnivore increasing their roadkill risk. Our results showed the need to consider the characteristics such as the availability of prey or the surrounding habitat, as well as intrinsic characteristics of the road type when studying wildlife roadkills as the road-type-mediated patterns demand-specific mitigation measures.
... The Iberian Peninsula is a mountainous region and the mountain ranges limit gene flow in other vertebrates (e.g., amphibians, Sánchez-Montes et al. 2018;lizards, Horreo et al. 2019) and mountains structure mustelid populations elsewhere (e.g., American badger [Taxidea taxus], Kyle et al. 2004 (Santos et al. 2009), which can lead to high roadkill rates when polecats visit road embankments where rabbits occur high numbers (Barrientos and Bolonio 2009, Barrientos and Miranda 2012. As rabbits are widespread in the Iberian Peninsula, sampling road-killed polecats in road verges where rabbits are abundant very likely provides a good coverage of the current distribution of this predator. ...
... They usually occur in marshlands and flooded meadows in Girona (Palazón et al. 2010) and neighboring France (Lodé 1993(Lodé , 1994, where they mainly feed on voles and anurans (Lodé 1993(Lodé , 1994. In contrast, in Mediterranean areas of Spain and Portugal, the species selects dry crops with a high diversity of habitat characteristics, scrublands of yellow broom (Retama sphaerocarpa) and alfa grass (Stipa tenacissima), and riparian vegetation, often in non-permanent water courses (Mestre et al. 2007, Barrientos and Bolonio 2009, Barrientos and Miranda 2012, where rabbits, its main prey here (Santos et al. 2009), are particularly abundant. Before accomplishing any translocation or stocking (which can also encompass artificial selection; Horreo et al. 2008), the causes leading to population decline must be clearly identified and reversed or population reinforcement will fail (Fischer and Lindenmayer 2000). ...
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Volunteer‐based roadkill monitoring schemes, including road carcass sampling, can represent considerable advances with respect to classical methods employed in conservation biology. We studied the genetic diversity, structure, and dynamics of the European polecat ( Mustela putorius ) across the Iberian Peninsula. We used samples of road carcasses collected by volunteers because this carnivore is an elusive species otherwise difficult to monitor with standard field protocols. We gathered 238 samples obtained from 2004 to 2022 from 13 different areas (8–31 samples/area). Using microsatellite loci, we identified 4 genetic units with gene flow among 3 of them in the Iberian Peninsula. The genetic variability was steadily low in 1 of the areas (Girona) for all the parameters evaluated. This area is also genetically isolated from the other studied areas. The inbreeding coefficient was significant in the north‐ and south‐Iberia units, and we did not detect a bottleneck signature in any of the 4 genetic units. Future conservation actions should consider the genetic dissimilarity among detected units and elucidate the ecological factors that have led to the observed genetic patterns.
... Increase: [5,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] Decrease (barrier to movement): [12,26,29,36, Crossing ability [13,25,39,56,57,[75][76][77][78][79][80][81][82][83][84][85] Morphological and life-history traits: mobility, body size, diet type, gender, age, health, body length, group size, various road uses (e.g., foraging or thermoregulation on roads), reproductive and breeding behaviors, seasonal migrations, post-breeding dispersal, movements to hibernation locations [2,5,29,31,33,34,80,84,85,[92][93][94][95][96][97][98][99][100][101] Numerous studies have found that the local population density is a crucial factor influencing WVCs [86][87][88][89][90][91]. As mentioned previously, larger species and carnivores are likely to be more mobile, but the specific effects on WVCs must be considered in combination with other factors. ...
... Various road uses, such as foraging (e.g., predation, scavenging), movement, and thermoregulation, can increase the density and road crossing of wildlife, which may enhance the risk of WVCs [2,5]. For example, the presence of prey on roadsides may increase the risk of WVCs of predators [94,97]. In northern latitudes, just after dawn, birds often sit on tarmac roads, which are warmer than dew-laden roadside vegetation. ...
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Road mortalities caused by wildlife–vehicle collisions (WVCs) are the most obvious negative effect of roads on wildlife. Identifying the influencing factors and summarizing the spatial-temporal patterns of WVCs have been important research trends in recent decades. However, most studies have only considered a portion of the factors, and there remains a lack of a relatively complete framework, including the numerous factors of WVCs, as well as the underlying transmission mechanisms between factors. In this study, an analytical framework incorporating a wide range of previously discussed factors is constructed. The framework not only displays the possible direction of the influence of each factor on WVCs, but also summarizes some important potential explanations under some circumstances and reveals the main interactions between certain types of factors. From one perspective, the factors affecting WVCs can be divided into four categories: species characteristics, road and traffic characteristics, landscape and environmental characteristics, and driver-related factors and specific human activities. From another perspective, the factors affecting WVCs can be mainly categorized as those related to entering roads and those related to leaving roads safely. The study begins with a discussion of three important sub-frameworks: factors promoting road crossing, factors related to barriers to movement, and factors related to safe crossing. Finally, a suggestion is provided to promote the research on WVCs globally.
... Highly mobile species are more susceptible to vehicle collision (Arévalo et al., 2017). Trophic webs in vertebrates are useful to understand human effect in roadkill dynamics such as predation features along roads (Barrientos and Bolonio, 2008). Numerous studies also indicated that prey availability is associated with roadkill in carnivore animals. ...
... Extensive research on WVC patterns has demonstrated that these collisions are not random events but tend to cluster spatially [5,[22][23][24][25]. Studies focusing on the causes of WVC have typically unveiled the influence of traffic characteristics, highway attributes, and habitat usage, employing diverse statistical models [26][27][28]. A literature review conducted by Gunson et al. [27] uncovered that the presence of forest and open habitats near roads tends to elevate the frequency of ungulate collisions. ...
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Simple Summary This study evaluates the effectiveness of an ecological bridge, constructed in 2020 in the Zeytinler neighborhood, as a solution to mitigate wild-boar–vehicle collisions (WVCs) on the Izmir-Çeşme motorway. The Zeytinler Ecological Bridge was monitored and analyzed for wildlife crossings, particularly by wild boars. Between August 2020 and December 2022, 686 instances of movement were observed among six wild mammal species, with wild boars representing 87.5% and foxes 10% of the recorded crossings. The study indicates that the highest frequency of wildlife crossings occurred in autumn between 22:00 and 02:00, coinciding with specific moon phases. Moreover, wild boar crossings increased during the autumn season with a full pond, and no wild boar fatalities were recorded after the bridge’s completion. The findings suggest that the ecological bridge effectively facilitates safe wildlife passage, especially for wild boars, reducing the risk of collisions with vehicles on the motorway. Abstract In this study, the use of an ecological bridge installed as a wildlife overpass and constructed in the Zeytinler neighborhood in 2020 was analyzed as a mitigating factor in wild-boar–vehicle collisions (WVCs) on the Izmir-Çeşme motorway. In this context, this study aimed to assess the use of the Zeytinler Ecological Bridge by wild boars (Sus scrofa Linnaeus, 1758). To this end, wildlife crossings were monitored, analyzed, and modeled with Bayesian networks. Between August 2020 and December 2022, a total of 686 instances of movement were observed among six medium to large wild mammal species. Wild boars accounted for approximately 87.5% of the recorded wildlife crossings, with foxes comprising 10%. The findings showed that the highest frequency of wildlife crossings occurred during the autumn season, particularly between 22:00 (10 p.m.) and 02:00 (2 a.m.), coinciding with the Waxing Gibbous and Waxing Crescent phases of the moon. The model outcomes highlighted that during the autumn season with a full pond, wild boar crossings increased by one and a half times in comparison to regular herd crossings. Throughout the observation period, there were no instances of wild boar fatalities subsequent to the completion of the bridge.
... This fact could also explain the presence of AVC-killed barn owl individuals, known to be rodent predators, at the same sites. According to Little et al. [27] and Barrientos and Bolonio [28], predator AVCs are often related to the presence of the prey in the same environment, which is likely to be related to the use of such roads' verges by predators as hunting/scavenging places. Similar conclusions were presented by Rytwinski and Fahrig [29] about carrion-eating birds, and Common Raven, for example, was found to be in contact with dead animals in our study. ...
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Animal-vehicle collisions (AVCs) are considered a major cause of wildlife mortality. This study identified carcasses of AVC-killed animals along a road network located south of Algiers. The results showed that different types of wild and domestic animals were killed by vehicles, mostly mammals. In terms of diversity, wild animals are more subjected to AVC than domestic ones. But, dogs were the most commonly reported. Hyaena hyaena, declared a threatened species, was also reported in this AVC study. Moreover, the road types’ diversity and seasonal variation also affected the AVC-affected animals. More collaboration between fauna conservationists and road authorities seems to be urgent to reduce AVC in Algeria.
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Landscape ecology and conservation biology are rapidly developing disciplines, and a current synthesis of principles and applications in these two fields is needed under one cover. Many managers are not applying principles of landscape ecology in efforts to conserve biota, yet the loss of biological diversity could be reduced if broad-scale processes and patterns were consistently considered in management and conservation decisions. Bringing together insights from leaders in landscape ecology and conservation biology, this book explains how our knowledge about landscape ecology can help us understand, manage and maintain biodiversity. Beyond explaining pertinent concepts of landscape ecology and biological conservation and describing examples of their use in management, research and planning, this book also distills principles for applying landscape ecology in conservation, identifies gaps in current knowledge and provides research approaches to fill those voids. The book is divided into five parts: the first part introduces the book and discusses what landscape ecology is and why it is important to biological conservation. The second deals with multiple scales, connectivity and organism movement. The third part discusses landscape change and how this affects biodiversity, and the fourth part covers conservation planning. The final part presents a synthesis that identifies overarching principles, pervasive constraints and realistic prospects for applying landscape ecology in biological conservation. Conservationists, land-use planners, and ecologists will find this book to be an essential resource. Foreword by Richard T.T. Forman.
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