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‘Animals under wheels’: Wildlife roadkill data collection by citizen scientists as a part of their nature recording activities


Abstract and Figures

‘Animals under wheels’ is a citizen science driven project that has collected almost 90,000 roadkill records from Flanders, Belgium, mainly between 2008 and 2020. However, until now, the platform and results have never been presented comprehensively to the scientific community and we highlight strengths and challenges of this system. Data collection occurred using the subsite (‘animals under wheels’) or the multi-purpose biodiversity platform and the apps, allowing the registration of roadkill and living organisms alike. We recorded 4,314 citizen scientists who contributed with at least a single roadkill record (207-1,314 active users per year). Non-roadkill records were registered by 85% of these users and the median time between registration of the first and last record was over 6 years, indicating a very high volunteer retention. Based on photographs presented with the roadkill records (n = 7,687), volunteer users correctly identified 98.2% of the species. Vertebrates represent 99% of all roadkill records. Over 145,000 km of transects were monitored, resulting in 1,726 mammal and 2,041 bird victims. Carcass encounter rates and composition of the top 10 detected species list was dependent on monitoring speed. Roadkill data collected during transects only represented 6% of all roadkill data available in the dataset. The remaining 60,478 bird and mammal roadkill records were opportunistically collected. The top species list, based on the opportunistically collected roadkill data, is clearly biased towards larger, enigmatic species. Although indirect evidence showed an increase in search effort for roadkill from 2010-2020, the number of roadkill records did not increase, indicating that roadkills are diminishing. Mitigation measures preventing roadkill could have had an effect on this, but decrease in population densities was likely to (partially) influence this result. As a case study, the mammal roadkill data were explored. We used linear regressions for the 17 most registered mammal species, determining per species if the relative proportion per year changed significantly between 2010 and 2020 (1 significant decrease, 7 significant increases). We investigated the seasonal patterns in roadkill for the 17 mammal species, and patterns per species were consistent over the years, although restrictions on human movement, due to COVID-19, influenced the seasonal pattern for some species in 2020. In conclusion, citizen scientists are a very valuable asset in investigating wildlife roadkill. While we present the results from Flanders, the platform and apps are freely available for projects anywhere in the world.
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‘Animals under wheels’: Wildlife roadkill data
collection by citizen scientists as a part of their nature
recording activities
Kristijn R.R. Swinnen1, Annelies Jacobs1, Katja Claus2, Sanne Ruyts1,
Diemer Vercayie1, Jorg Lambrechts1, Marc Herremans1
1Natuurpunt Studie, Mechelen, Belgium 2Department of Environment and Spatial Development, Brussel, Belgium
Corresponding author: Kristijn R.R. Swinnen (
Academic editor: Sara Santos|Received 13 August 2021|Accepted 27 December 2021|Published 25 March 2022
Citation: Swinnen KRR, Jacobs A, Claus K, Ruyts S, Vercayie D, Lambrechts J, Herremans M (2022) ‘Animals under
wheels’: Wildlife roadkill data collection by citizen scientists as a part of their nature recording activities. In: Santos S,
Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature
Conservation 47: 121–153.
Animals under wheels’ is a citizen science driven project that has collected almost 90,000 roadkill records
from Flanders, Belgium, mainly between 2008 and 2020. However, until now, the platform and results
have never been presented comprehensively to the scientic community and we highlight strengths and
challenges of this system. Data collection occurred using the subsite (‘ani-
mals under wheels’) or the multi-purpose biodiversity platform and the apps, allowing the
registration of roadkill and living organisms alike. We recorded 4,314 citizen scientists who contributed
with at least a single roadkill record (207-1,314 active users per year). Non-roadkill records were registered
by 85% of these users and the median time between registration of the rst and last record was over 6
years, indicating a very high volunteer retention. Based on photographs presented with the roadkill re-
cords (n = 7,687), volunteer users correctly identied 98.2% of the species. Vertebrates represent 99% of
all roadkill records. Over 145,000 km of transects were monitored, resulting in 1,726 mammal and 2,041
bird victims. Carcass encounter rates and composition of the top 10 detected species list was dependent
on monitoring speed. Roadkill data collected during transects only represented 6% of all roadkill data
available in the dataset. e remaining 60,478 bird and mammal roadkill records were opportunistically
collected. e top species list, based on the opportunistically collected roadkill data, is clearly biased to-
wards larger, enigmatic species. Although indirect evidence showed an increase in search eort for roadkill
from 2010-2020, the number of roadkill records did not increase, indicating that roadkills are diminish-
Nature Conservation 47: 121–153 (2022)
doi: 10.3897/natureconservation.47.72970
Copyright Kristijn R.R. Swinnen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License
(CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Launched to accelerate biodiversity conservation
A peer-reviewed open-access journal
Kristijn R.R. Swinnen et al. / Nature Conservation 47: 121–153 (2022)
ing. Mitigation measures preventing roadkill could have had an eect on this, but decrease in population
densities was likely to (partially) inuence this result. As a case study, the mammal roadkill data were
explored. We used linear regressions for the 17 most registered mammal species, determining per species
if the relative proportion per year changed signicantly between 2010 and 2020 (1 signicant decrease,
7 signicant increases). We investigated the seasonal patterns in roadkill for the 17 mammal species, and
patterns per species were consistent over the years, although restrictions on human movement, due to
COVID-19, inuenced the seasonal pattern for some species in 2020. In conclusion, citizen scientists
are a very valuable asset in investigating wildlife roadkill. While we present the results from Flanders, the
platform and apps are freely available for projects anywhere in the world.
Citizen science, data quality, mammals, presence only data, relative trends, roadkill, structured monitor-
ing, seasonal patterns
Roads directly impact populations and species due to vehicle induced mortality. An
estimated 29 million mammals and 194 million birds are killed annually on European
roads (Grilo et al. 2020). Worldwide, all mortality sources considered, natural or hu-
man, vehicle induced mortality was 7% for adult mammals and 1% for adult birds
(Hill et al. 2019).
Apart from direct mortality by wildlife vehicle collisions, roads and trac do have
multiple eects on ecosystems and wildlife populations including habitat loss and
habitat fragmentation (Taylor and Goldingay 2010; Whittington et al. 2019). Roads
can have genetic eects by acting as a barrier and decreasing genetic diversity (Cof-
n 2007;Holderegger and Di Giulio 2010). Furthermore, the presence of roads, and
the intensity of their use, can result in behavioural changes of individuals and species
(Mumme et al. 2000; Kerley et al. 2002; Whittington et al. 2019).
Monitoring of wildlife roadkill can, apart from the collection of the numbers being
killed, facilitate monitoring of population trends, species distribution and invasions,
animal behaviour and contaminants and disease (Schwartz et al. 2020). Volunteer
citizen scientists can collect and/or process data as part of a scientic inquiry (Silver-
town 2009) and they play an important role in the data collection of roadkill records
in projects which have been initiated worldwide (Shilling et
al. 2015). Globally, there are dozens of web based systems to register wildlife vehicle
collision casualties or roadkill (Shilling et al. 2015). Citizen science data on roadkill
has proven to be a valuable data source for the identication of potential roadkill
hotspots (Shilling and Waetjen 2015; Périquet et al. 2018; Engleeld et al. 2020),
temporal patterns in roadkill (Raymond et al. 2021) and species range maps (Tiede-
man et al. 2019). Long term motivation of volunteers, support for the identication
of roadkill and feedback to volunteers are of critical importance in sustaining roadkill
citizen science projects (Bil et al. 2020). e Flemish project ‘Animals under wheels’
(Dieren onder de wielen) is one of the largest citizen science driven roadkill databases
Animals under wheels 123
worldwide (Waetjen and Shilling 2017). However, until now, the platform and results
have never been presented comprehensively to the scientic community. We high-
light strengths and challenges of this system, which is easily and freely available to
be deployed anywhere in the world for roadkill monitoring (and general biodiversity
monitoring as well).
We describe and analyse the roadkill data submitted to the online biodiversity data-
base, the local Flemish version of the international platform is platform allows for the registration of observations of all
plants, fungi and animals. Since the launch in 2008 until 2020, this resulted in more
than 26,200 species and 31,5 million observations for the 13,522 km2 of Flanders,
generating one of the densest biodiversity datasets in the world. Flanders is the north-
ern region of Belgium, situated in Western Europe. It has a very high human density
of 487 inhabitants/km2 (Statbel 2020) and 5.08 km of roads/km2, one of the densest
road systems in the whole of Europe (Vercayie and Herremans 2015). Flanders has
883 km of motorways, 6,040 km of regional roads and 64,080 km of local roads (FPS
Mobility and Transport 2011). We show the 2019 trac data since this is the last year
without a COVID-19 impact. Daily, over 70 million vehicle kilometres are driven on
Flemish motorways (Hoornaert 2019) and the monitoring of 880 motorway segments
indicated an average daily trac volume of 37,592 vehicles per segment per day (me-
dian = 32,067, min = 4,440 and max = 131,508) (Vlaams Verkeerscentrum 2021). On
regional roads, the monitoring of 127 segments showed an average daily trac volume
of 17,583 vehicles per segment per day (median = 16,666, min = 2,381 and max =
36,649). For local roads, the authors are not aware of available data. e most recent
available data from 2017 indicate the Flemish registered vehicles drive 61.1 billion
kilometre per year (Kwanten 2018).
Roadkill data in the database can be submitted using: (a) the
online platform, (b) the subsite www.dierenonderdewielen.
be (‘animals under wheels’) or (c) the apps ObsMapp for Android, iObs for iPhone
and recently ObsIdentify for all devices. On the online platform, the location of the
observation must be pinpointed on the map, date/time selected and species and ad-
ditional observation information ‘roadkill’ label must be selected using controlled vo-
cabulary (Waetjen and Shilling 2017). In the apps, location and time are derived from
the smartphone. Species and ‘roadkill’ must be selected using controlled vocabulary in
the appropriate data elds. Photographs and additional information can be added to
an observation but are not mandatory. e apps do also function in a voice recognition
mode to register observations, which is always useful, but essential when monitoring
during driving (Vercayie and Herremans 2015).
We analyse the number of users registering roadkill records, the active users per
year and the number of new users per year (recruitment) to show the long-term vi-
Kristijn R.R. Swinnen et al. / Nature Conservation 47: 121–153 (2022)
ability of the project. We investigate the number of roadkill records per user and the
distribution between users including the corresponding Gini coecient, a measure
of unevenness (0: totally equal, 1: a single person is responsible for all records) (Sau-
ermann and Franzoni 2015). We calculate the retention time per user, dened as the
time between the registration of the rst and the last roadkill per user. For all roadkill
registering citizen scientists, we examine if they also registered observations of plants,
fungi or living wildlife within the database.
Data quality
Quality control of the data is an important step in all scientic processes, and also
very important for citizen science projects (Wiggins et al. 2011). e data validation
procedure in the ‘’-database combines species specialists (experienced
volunteers) assigning a validation status to observations and an algorithm automati-
cally evaluating observations. is multi-step process depends on the proof presented
(not mandatory but possible), species status (common vs rare), location and time (was
there already a proven record of presence within a species group dependent dened
range of space and time) of the observation (Swinnen et al. 2018). Species specialists
can assign a validation status to an observation: (a) ‘Approved (based on evidence)’,
evidence can be a picture or sound, (b) ‘Approved (based on expert judgement)’, the
additional information or the knowledge of the observer makes it highly likely this is
a correct observation, (c) ‘Under review’, temporary status, no decision has been taken
yet, (d) ‘Cannot be assessed’, proof or explanation does not allow for a decision to be
made, (e) ‘Rejected’, observation was wrong and user does not correct it. e algo-
rithm can also assign a validation status: (f) ‘Automatic validation’, for a record to be
automatically validated, there need to be a number of earlier observations of the species
supported by proof (at least one or two), within a certain radius (ranging from 100 m
to 10 km) within a specied time range (60–3000 days). Remaining observations are
classied (g) ‘unveried’. e validation process is an interactive process where users
can be contacted for additional information or suggested to change the species name
or other details in case of an error. We investigate the possible error ratio by calculating
the percentage of approved observations (based on photographic evidence) which was
initially wrong but corrected by the user after interaction with a validator.
Methodology of data collection
To allow standardised data collection and a quantiable measure of search eort, two
options for data registration are oered to users. In 2013, the option to gather stand-
ardised transect data was added to the website. Users were asked to choose a specic
route, draw it online and check it at least once every two weeks, but not more than
once a day. ey were asked to ll in the survey, even if no roadkill was detected. ese
type of transects are called xed transects in this manuscript. Since 2018, smartphone
users can allow their app to register their transect while observing nature and register-
Animals under wheels 125
ing observations. When nished, users indicate per species group if their transect can
be used as a roadkill monitoring transect. Since there are no requirements for transects
to be identical, or to be repeated over time, we call them variable transects.
For the xed transects, users register the transport modus (on foot, by bike, by car).
For the variable transects, the transect is recorded by the smartphone and we derived
the speed from the track length and duration, and classied transects as 0–7 km/h as
on foot, 7–25 km/h by bike and >25 km/h by car (although another motorised vehicle
is also possible). is distinction according to speed is important because speed aects
detection probability and it is known that searching on foot is more eective than
counting while driving (Slater 2002). Data collected during standardised monitoring
contains more information but it is also more demanding for volunteers resulting in a
smaller number of participants (Bonney et al. 2009). is mainly used as a personal notebook by naturalists to register
and document their sightings. Although some users are aware of the additional scien-
tic advantages standardised data collection oers, the majority of all observations in are presence only records (also known as roving records) (Vercayie
and Herremans 2015). Given the correct identication of the species, presence is con-
rmed but search eort is unknown. e absence of a record can have multiple causes:
no roadkill present, no observer present or both present but not registered by the ob-
server. We show a summary of the transect data including transect characteristics and
top 10 of recorded bird and mammal species and calculate the average distance that
needs to be covered to encounter a roadkill. For the presence only data, a top 20 for
bird and mammal casualties is presented and we compare the results with the data col-
lected during transect counts. While herpetofauna is also an important species group,
e.g. because of their worldwide threatened status (Heigl et al. 2017), we do not discuss
them here since they are only recorded at lower driving speeds, and a larger (roadkill)
database, separate from is available, calling for a specic analysis.
Case study: mammal roadkill records
e number of new observations (of all organisms) submitted to
continues to increase year after year, from 400,000 in 2008 to over 6,000,000 in 2020
(and over 8.7 million in 2021). For 2010–2020 we investigate by means of a linear re-
gression (R Core Team 2016): (a) is there an increase in mammal roadkill observations?
(b) is there an increase in mammal observations (excluding all automated observations
by camera traps and bat-detectors since they do not represent human search eort)?
(c) are both correlated?
e large majority of roadkill data is collected as presence only data. Since search
eort is unknown, absolute roadkill trends per species cannot be calculated. However,
relative trends can be calculated and give an indication of the increase or decrease of
roadkill abundance of a specic species compared to the other species killed on the
road. For this analysis all mammal roadkill records were combined (presence only and
transect data), excluding observations where observers indicated they were uncertain
Kristijn R.R. Swinnen et al. / Nature Conservation 47: 121–153 (2022)
of species determination (1.5% of observations), and only species with a minimum of
50 roadkill individuals were withheld, resulting in 17 species (only species level records
were considered). Per species, the percentual abundance per year from these 17 species
was calculated. By using a linear regression, we determine per species if the relative
proportion per year changed signicantly between 2010 and 2020. Graphs were made
using ggplot 2 (R Core Team 2016; Wickham 2016). Based on unstructured, presence
only, citizen science data on roadkill, we propose the relative change in proportion of
roadkill victims as a means to gain insight in relative population changes as roadkill
numbers are expected to be strongly and positively associated with the local abundance
of living animals (Baker et al. 2004; George et al. 2011; Pettett et al. 2018; Schwartz
et al. 2020).
Apart from the local abundance, timing within the year does inuence the number
of victims found. Animals are sensitive to wildlife vehicle collisions during movement.
is can be daily movement while foraging or patrolling home ranges, or seasonal-
ity in mating, juvenile dispersal or migration (Taylor and Goldingay 2010; Garriga
et al. 2017; Schwartz et al. 2020). For all roadkill data combined (presence only and
transect data) we plot species specic density functions using ggplot 2 (R Core Team
2016; Wickham 2016). For this, the number of records was used, and not the number
of individuals. Overall, 98.7% of records comprises a single individual, but more than
one individual is also sometimes reported. is can reect reality, multiple individuals
killed at once or, sometimes, users combine a number of observations from a timespan
from the same location and add a single observation to the database. Analysing these
‘combination records’ as if all individuals were killed at the same time would introduce
errors in this seasonal pattern and to avoid this, the number of observations was used.
For species with more than 1,000 records, we show the annual seasonal pattern in
roadkill data. When fewer data are available, a single density plot combining the data
from 2010–2020 is shown.
Within Flanders, 89,276 roadkill records were registered from 1960–2020 (Fig. 1).
Mammals (52,847), birds (23,346) and herpetofauna (11,762) represent 99% of road-
kill observations. Coleoptera (n = 499) is the invertebrate group with the most roadkill
records. One record can contain multiple individuals. Most records (93%) date from
2008 onwards, the launch of e majority of ‘historical’ records
(79%) were added by a single account (Regional Mammal Workgroup).
A total of 4,314 citizen scientists submitted at least one roadkill record from Flan-
ders (Fig. 2). Male roadkill registering volunteers (1,547) are three times as abundant
compared to females (457). For 2,310 citizen scientists the sex is unknown. On average
881 users were active per year (range 207–1,314) and this number shows a steady in-
crease. Per year, on average 332 (range 207–465) ‘new’ users register their rst roadkill
victim with an increase of 20% in 2020 compared to the second best year (2009).
Animals under wheels 127
Contributions of users are unequal with 44.4% of users only registering a single
roadkill record (see Table 1). e median number of roadkill records per user is 2 (aver-
age 21, range 1–4,931). e Gini coecient of inequality between users is 0.87.
When including all roadkill registering users, volunteer retention time, i.e. the
median time between registration of rst and last roadkill record, is 7 days. For users
Figure 2. the number of active roadkill registering users per year in Flanders and the number of rst
time roadkill registering users per year in Flanders since 2008, the launch of
until 2020.
Figure 1. Roadkill observations per decade (1960-1999) or per year (2000-2020) and cumulative num-
ber of roadkill observations in Flanders, Belgium.
Kristijn R.R. Swinnen et al. / Nature Conservation 47: 121–153 (2022)
with only a single roadkill record, we consider this single record as the rst and the last
record and the time between records was 0 days. When excluding the users with only
a single roadkill record, the median volunteer retention time increases to over 4 year
(1,501 days).
e majority of roadkill recorders (85%) did also submit non-roadkill observa-
tions to the biodiversity database and together they are responsible for 25.9 million
non-roadkill observations (on a total of 31.5 million non-roadkill records by 49,447
users registered in 2008–2020 in Flanders). is indicates that for most users, the
registration of roadkill is a natural part of their registration of nature observations, but
the focus is rarely on roadkill alone. When calculating the median volunteer retention
time of citizen scientists which registered at least a single roadkill record, based on all of
their observations, roadkill and living organisms together, this exceeds 6 years (2,318
days, range 0–5,243 days).
Data quality
In total, 38.9% of records were approved based on dierent procedures (Table 2). For
all observations approved based on the presented photographic evidence, only 139 out
of 7,687 recordings needed to be corrected by the validator. is results in an error rate
of 1.8%. In only a very small percentage of cases, users do not respond to suggestions
to change the species and the observation is then rejected.
All observations which were rejected, under review or which cannot be assessed are
removed in the following analyses.
Table 1. e amount of roadkill observations in 8 classes and the number of users in each class, including
the percentage of users per class.
Roadkill observations Users % of users
1 1,914 44.4%
2-5 1,254 29.1%
6-10 368 8.5%
11-20 267 6.2%
21-50 258 6.0%
51-100 114 2.6%
101-500 109 2.5%
501-5000 30 0.7%
Table 2. Validation status of the roadkill recordings in Flanders (1960-2020).
Validation status Number of observations (%)
Approved (based on evidence) 7,687 (8.61%)
Approved (based on expert judgement) 10,951 (12.27%)
Approved (automatic procedure) 16,062 (17.99%)
Under review 16 (0.02%)
Rejected 42 (0.05%)
Cannot be assessed 288 (0.32%)
Unveried 54,230 (60.74%)
Animals under wheels 129
Methodology of data collection
Transect data
We registered 309 xed transects online since the start in 2013 until 2020. A little
under half (148) were registered online but never monitored by the user. e remain-
ing 161 transects were monitored at least once, resulting in 2,521 records of bird and
mammal roadkill during 59,256 km of monitoring. In Table 3 we show the xed tran-
sect characteristics and results grouped per transport mode.
We registered 4,778 variable transects for bird and mammal roadkill since
2018, the year when the smartphone applications (ObsMapp and iObs) allowed
it, until the end of 2020. Each transect is considered unique since small variations
in the registration of the transect are present, resulting in no repeated counts per
transect. is resulted in 1,246 bird and mammal roadkill registrations while moni-
toring 86,235 km. In contrast with the xed transects, it is possible the user only
monitors a single species group. erefore, mammal and bird transects are shown
separately in Table 4.
When combining both transect types 3,767 roadkill records were registered. For
birds, carcass encounter rates vary from 1 carcass per 75.7 km on foot, 1 carcass per
59.3 km by car to 1 carcass per 34.6 km by bike. For mammal, carcass encounter
rates are similar, 1 carcass per 74.7 km on foot, 1 carcass per 70.7 km by car and 1
carcass per 43.5 km by bike. We show the top 10 of most frequently recorded (wild)
roadkill species for birds and mammals while monitoring transects by car (Table 5)
and bike (Table 6). We include observations not identied to species level, but they
are unranked.
Table 3. Fixed transect characteristics and results grouped per transport mode (2013-2020). * A single
transect can be monitored on foot, by bike and by car. ats why the sum of the dierent transects diers
from 161.
Distance (km) Dierent
# counts Median # counts
per transect
Average # counts
per transect (range)
Roadkill Birds
Roadkill Mammals
By car 32,673 103 2,722 8 26 (1-484) 581 497
By bike 26,063 92 4,815 16.5 52 (1-1,204) 782 636
On foot 520 31 299 1 10 (1-70) 15 10
Table 4. Variable transect characteristics and results grouped per transport mode (2018-2020).
Distance (km) Dierent transects Roadkill victims
By car Birds 36,999 1,570 593
By car Mammals 39,910 1,723 529
By bike Birds 2,943 262 57
By bike Mammals 3,137 285 35
On foot Birds 1,600 461 13
On foot Mammals 1,646 477 19
Kristijn R.R. Swinnen et al. / Nature Conservation 47: 121–153 (2022)
Table 5. Top 10 of birds and mammal roadkill victims encountered the most frequently by car during
transect monitoring. Observations not identied to species level are shown but not ranked.
Birds Scientic name Common name # ind.
1Columba palumbus Common wood pigeon 329
Aves unknown Bird unknown 286
2Turdus merula Common blackbird 172
3Phasianus colchicus Common pheasant 74
4Anas platyrhynchos Mallard 37
5Corvus corone Carrion crow 30
6Buteo buteo Common buzzard 20
7Pica pica Eurasian magpie 18
8Coloeus monedula Western jackdaw 17
9Gallinula chloropus Common moorhen 17
10 Strix aluco Tawny owl 16
Mammals Scientic name Common name # ind.
Mammalia unknown Mammal unknown 270
1Erinaceus europaeus Hedgehog 223
2Lepus europaeus European hare 97
3Rattus norvegicus Brown rat 79
4Oryctolagus cuniculus European rabbit 70
5Martes foina Beech marten 67
6Sciurus vulgaris Red squirrel 39
6Vulpes vulpes Red fox 39
8Mustela putorius European polecat 18
Rattus unknown Rat unknown 7
9Capreolus capreolus Roe deer 5
Mustelidae unknown Marten unknown 5
10 Talpa europaea European mole 3
Table 6. Top 10 of birds and mammal roadkill victims encountered the most frequently by bike during
transect monitoring. Observations not identied to species level are shown but not ranked.
Birds Scientic name Common name # ind.
1Turdus merula Common blackbird 256
2Columba palumbus Common woodpigeon 169
Aves unknown Bird unknown 58
3Phasianus colchicus Common pheasant 46
4Anas platyrhynchos Mallard 28
5Coloeus monedula Western jackdaw 28
6Gallinula chloropus Common moorhen 24
7Passer domesticus House sparrow 24
8Erithacus rubecula European robin 23
9Streptopelia decaocto Eurasian collared dove 22
10 Parus major Great tit 20
Mammals Scientic name Common name # ind.
1Erinaceus europaeus Hedgehog 182
2Rattus norvegicus Brown rat 144
3Oryctolagus cuniculus European rabbit 71
4Lepus europaeus European hare 52
5Sciurus vulgaris Red squirrel 29
Mammalia unknown Mammal unknown 22
6Apodemus sylvaticus Wood mouse 14
6Martes foina Beech marten 14
Muridae unknown Mouse/rat unknown 12
Soricidae unknown Shrew unknown 12
8Talpa europaea European mole 11
Rattus unknown Rat unknown 10
Rodentia unknown Rodent unknown 10
Microtidae unknown Vole unknown 8
9Vulpes vulpes Red fox 6
10 Crocidura russula Greater white-toothed shrew 5
Animals under wheels 131
Presence only data
A total of 20,638 bird victims and 39,849 mammal victims were registered in waarne- from 2010–2020. Consequently, 94% of all roadkill records from 2010–
2020 are presence only data. We show the top 20 in Table 7.
Table 7. Top 20 of most registered bird and mammal roadkill victims which are collected as presence only
records. Observations not identied to species level are shown but not ranked.
Birds Scientic name Common name # ind.
1Turdus merula Common blackbird 3,686
2Columba palumbus Common woodpigeon 3,624
3Anas platyrhynchos Mallard 1,411
4Phasianus colchicus Common pheasant 1,294
5Tyto alba Western barn owl 926
6Strix aluco Tawny owl 817
Aves unknown Bird unknown 766
7Gallinula chloropus Common moorhen 761
8Buteo buteo Common buzzard 728
9Pica pica Eurasian magpie 504
10 Passer domesticus House sparrow 404
11 Coloeus monedula Western jackdaw 402
12 Athene noctua Little owl 333
13 Corvus corone Carrion crow 267
14 Streptopelia decaocto Eurasian collared dove 248
15 Asio otus Long-eared owl 234
16 Erithacus rubecula European robin 213
17 Garrulus glandarius Eurasian jay 212
18 Falco tinnunculus Common kestrel 194
19 Larus argentatus European herring gull 193
20 Turdus philomelos Song thrush 175
Mammals Scientic name Common name # ind.
1Erinaceus europaeus Hedgehog 12,147
2Vulpes vulpes Red fox 5,353
3Sciurus vulgaris Red squirrel 3,779
4Martes foina Beech marten 3,619
5Mustela putorius Western polecat 2,591
6Oryctolagus cuniculus European rabbit 2,569
7Lepus europaeus European hare 2,148
8Rattus norvegicus Brown rat 2,108
9Capreolus capreolus Roe deer 855
Mammalia unknown Mammal unknown 488
10 Talpa europaea European mole 317
Mustelidae unknown Marten unknown 287
11 Meles meles Eurasian badger 283
12 Mustela nivalis Least weasel 232
13 Mustela erminea Stoat 186
Martes foina/martes Beech/Pine marten 171
14 Sus scrofa Wild boar 137
Rattus unkown Rat unknown 74
15 Castor ber Eurasian beaver 67
16 Martes martes Pine marten 65
17 Apodemus sylvaticus Wood mouse 63
18 Crocidura russula Greater white-toothed shrew 59
19 Pipistrellus pipistrellus Common pipistrelle 46
20 Mus musculus House mouse 40
Kristijn R.R. Swinnen et al. / Nature Conservation 47: 121–153 (2022)
Mammal case study
We compare the number of non-roadkill mammal observations (one observation can con-
tain multiple individuals) with the number of mammal roadkill observations (transect and
present only data combined) annually from 2010–2020 in Flanders, Belgium (Table 8).
Over the years, there is a signicant increase in non-roadkill mammal observations
(slope = 9106, t = 4.49, p-value = 0.00150**) but no signicant increase in roadkill
registrations (slope = 118, t = 1.88, p-value = 0.09). ere is also no signicant cor-
relation between non-roadkill and roadkill mammal observations (slope = 0.008, t =
1.379, p-value = 0.201).
Table 9 shows the 17 mammal species with more than 50 roadkill individuals, the
outcomes from the linear regression between year (2010–2020) and the percentual
abundance per year.
Table 9. Outcome of the linear regression for the 17 most registered mammal species in Flanders from
2010-2020. Signicant codes in the p-value column: <0.1 . >0.05, <0.05 * > 0.01, <0.01 ** > 0.001,
<0.001 *** For common names, see Table 7.
Rank Species N slope Std. error t-value p-value
1Erinaceus europaeus 12,262 -0.051 0.325 -0.158 0.878
2Vulpes vulpes 5,193 -0.467 0.230 -2.029 0.073 .
3Sciurus vulgaris 3,769 0.047 0.131 0.358 0.728
4Martes foina 3,566 0.425 0.121 3.526 0.006 **
5Oryctolagus cuniculus 2,578 -0.339 0.170 -1.994 0.077 .
6Mustela putorius 2,514 -0.450 0.129 -3.500 0.007 **
7Rattus norvegicus 2,268 0.141 0.159 0.884 0.400
8Lepus europaeus 2,252 0.269 0.089 3.013 0.015 *
9Capreolus capreolus 798 0.147 0.046 3.165 0.012 *
10 Talpa europaea 328 0.023 0.024 0.961 0.362
11 Meles meles 275 0.119 0.035 3.431 0.007 **
12 Mustela nivalis 226 -0.004 0.012 -0.342 0.740
13 Mustela erminea 185 -0.004 0.013 -0.306 0.767
14 Sus scrofa 103 0.057 0.009 6.007 0.0002 ***
15 Apodemus sylvaticus 74 0.020 0.004 5.389 0.0004 ***
16 Castor ber 60 0.041 0.010 3.797 0.004 **
17 Martes martes 57 0.028 0.014 1.995 0.077 .
Table 8. Mammalian roadkill and non-roadkill observations per year and the percentage of roadkill com-
pared to all mammal observations from 2010-2020 in Flanders. Obs.= observations.
Year Mammal roadkill obs. Non-roadkill mammal obs. Mammal roadkill as % of total mammal obs.
2010 3,338 20,201 14.2%
2011 2,740 21,100 11.5%
2012 2,884 30,009 8.8%
2013 2,639 27,211 8.8%
2014 4,836 46,033 9.5%
2015 4,212 35,815 10.5%
2016 4,408 51,417 7.9%
2017 3,866 108,415 3.4%
2018 4,040 123,193 3.2%
2019 4,312 73,858 5.5%
2020 3,580 88,850 3.9%
Animals under wheels 133
Graphs showing percentual abundance per year per species are shown in Appendix
A. Mustela putorius is the only species with a signicant decreasing relative trend from
2010–2020. ere are seven species with an increasing relative trend, ordered here
from steepest to gentlest slope: Martes foina, Lepus europeaus, Capreolus capreolus, Meles
meles, Sus scrofa, Castor ber and Apodemus sylvaticus. Graphs showing seasonal patterns
in relative density per species for each year (2010–2020) are added to Appendix B.
Seasonal patterns in roadkill recordings dier clearly from species to species with most
species showing a bi- or unimodal pattern. When comparing the pattern from a single
species over multiple years, the consistency within the patterns is (very) good. Also the
species with fewer observations show mostly a clear seasonal pattern.
e detected and registered roadkill observations are only the tip of the iceberg. Even a
structured daily roadkill census underestimates the death rate (of smaller victims) with a
factor 12–16 (Slater 2002). Apart from the eect that roadkill has on wildlife (popula-
tions) there is also an economic cost. ere are no numbers available for Flanders, or the
whole of Europe, but wildlife-vehicle collisions in Spain cost 105 million € yearly (Sáenz-
de-Santa-María and Tellería 2015) while the animal-vehicle accidents with ungulates in
Sweden resulted in a cost of 275 million € in 2015 (Gren and Jägerbrand 2019).
For Flanders, Capreolus capreolus, Sus scrofa, Canis lupus and Castor ber are among
the heaviest wild mammals, but injury or even death of drivers or passengers can also
occur when crashing into, or trying to avoid, smaller animals (Langbein 2007). A bet-
ter understanding of roadkill is therefore in the best interest of wildlife and humans.
e amount of roadkill records increased heavily since the launch of https:// in 2008 and together, over 4,300 citizen scientists collected almost
90,000 roadkill records. Similar to crowd science user contribution patterns, a small
number of users contributed most of the recordings and the Gini coecient of 0.87
is very similar to the average crowd science Gini coecient of 0.85 Sauermann and
Franzoni (2015) calculated for 7 crowd science projects. e registration of roadkill
seems to be an integrated part of the nature observation and registration, for most
volunteers, since 85% of users did also register non-roadkill observations. e use of a
multi-purpose biodiversity platform has a positive eect on the retention time, which
is over 6 years for roadkill recorders in is long volunteer retention
time indicates that allowing the registration of all species groups, roadkill or not, us-
ing the tools the users are already familiar with, is a successful alternative, and possibly
even preferable to a single purpose data platform focussing on roadkill alone.
Some scientists may be sceptical about the data quality of records collected by
citizen scientists, although they have the potential to produce data with an accuracy
at least equal to professionals (Kosmala et al. 2016). We report a species identication
accuracy of roadkill recordings with photographs of 98% (n = 7,687) which is nearly
identical to the 97% presented by Waetjen and Shilling (2017). is high propor-
Kristijn R.R. Swinnen et al. / Nature Conservation 47: 121–153 (2022)
tion of correct species identication is an indication of the quality of the database.
However, we suspect species identication accuracy to be lower for records without
photographs since many of these identications are from driving vehicles. Although
more than 60% of observations are unveried, the majority of these observations are
‘common’ species, which are mainly registered by a limited group of experienced na-
ture observers, and there is no reason to assume ‘a priori’ that these records contain
more errors. Depending on the purpose of the analysis, dierent data selections can
be made but the increase in data quality by eliminating all possible errors does not
always compensate for the loss in data quantity (Van Eupen et al. 2021). Continuous
communication on the importance of photographs when registering roadkill aims to
increase the amount of veriable records in the future.
Differences in the most registered species depending on data collection method
In order to determine which species is killed the most in trac, standardised monitor-
ing is necessary. Our results indicate that for birds and mammal species, searching at
an intermediate speed from 7 to 25 km/h results in the highest number of carcasses
found. is is somewhat unexpected given that a slower speed should increase detec-
tion rates (Slater 2002). We suggest that the searching for roadkill carcasses was tted
into the routine of a number of people in the past years and that biking happens more
frequently next to busy roads, where more carcasses are present compared to walking,
which is more likely along calmer roads. Driving by car resulted in roughly the same
encounter rates of birds and mammals carcasses compared to walking, however due
to the higher speed, corpses not identied to species level are more numerous. Stop-
ping safely to identify the species is often not possible in Belgium and stopping on
motorways is forbidden (and dangerous) (minimum speed 70 km/h, maximum speed
120km/h). At this speed, identication at species level is frequently impossible.
e quality of transect data (with a standard protocol) is higher but it is more dif-
cult to nd volunteers to collect them (Bonney et al. 2009; Vercayie and Herremans
2015). As a consequence, they only represent 6% of all available roadkill data from
Flanders. Although informative for local situations, currently, this is too sparse for
region-wide analysis. e variable transects are promising in this respect because they
can be monitored anywhere and anytime, but they are currently not yet widely enough
adopted by the user community. It is also too early for a detailed analysis since they
were only launched in 2018. Additional promotion and awareness in the user commu-
nity of the applicability could boost the popularity of these variable transects.
ere is a clear dierence between the rank list of most observed species during
transects and the rank list of most observed species in the opportunistic data. When
comparing data collected by car and bike, it is clear that only larger species are regis-
tered from cars and a higher proportion was not identied on species level. For the
mammal data, all rank lists of most observed species are led by Hedgehogs (with the
exception of unidentied mammals which outrank them in species lists collected from
cars). Hedgehogs are frequently reported as trac victims in Western Europe (Huijser
Animals under wheels 135
and Bergers 2000; Pettett et al. 2018) and road mortality of Hedgehogs is expected to
be an important factor in their decline (Wright et al. 2020). Common blackbirds are
ranked third by monitoring from the car, but rst in the other lists. is is not unex-
pected since they had the highest predicted roadkill rate, 12 individuals/km/year, in the
model of Grilo et al. (2020) and are among the most frequently killed bird species in
Western Europe (Erritzoe et al. 2003). Even transect data must be interpreted with care.
Carcass persistence times and detection depend on size, with smaller animals being re-
moved faster by scavengers (Santos et al. 2011; Teixeira et al. 2013; Ratton et al. 2014).
Detection probability of larger mammals can also be inuenced since they are more
likely to be removed by maintenance workers or during police intervention at the site
of an accident. Data collected by these services can be an important addition to the data
collected by citizen scientists. Although proven to be a valuable data source (Grilo et al.
2009) additional steps need to be taken in Flanders to collect and centralise this data.
As expected, the ranking of victims collected as presence only data diers from the
rankings in the transect data: presence only data show a clear bias to larger species, but
possibly also species which are perceived as more interesting. Number two in the presence
only data ranking is Red fox, which ranks only 6th in transects by car, and 9th in transects
by bike. Foxes are infrequently seen alive, so, an encounter with a dead fox is for many
people special enough to report. e number three, Red squirrel ranked 6th in transects
by car and 5th in transects by bike. e Brown rat, the species encountered most frequent-
ly as roadkill (with exception from the Hedgehog) in transects by bike was only ranked 8th
in the presence only data list. is indicates that due to reporting bias the presence only
data should not be used to determine which species are killed the most in trac.
Mammal case study
From 2010–2020 there is a strong increase in the number of non-roadkill mammal
observations registered on but no signicant increase in registered
roadkill mammal observations. It is known that retention of volunteers can be chal-
lenging (Pocock et al. 2014; Shilling et al. 2015, 2020) but the number of observers
registering roadkill has never been higher than the past 3 years (see Fig. 2) and their
retention time on the platform exceeds 6 year. Volunteer participa-
tion depends also on repeated communication about the project. Over the last 3 years,
our own communication channels mentioned the project ‘animals under wheels’ in 23
newsletters, we provided 15 contributions to written magazines, made 2 promotion
videos and contributed to 10 national symposiums. Mainstream media wrote 47 arti-
cles about the project, and we gave 20 radio and 3 TV interviews (overview in Jacobs et
al. (2021)) on the subject. is indicates that the absence in increase in registered road-
kill mammals is not due to a reduction in observers/search eort but we believe that
this is a strong indication that the number of roadkill is diminishing. Additional stand-
ardised collected data could conrm/refute this hypothesis. If this reduction is caused
by eective road mitigation such as fencing, when possibly combined with crossing
structures or animal detection systems (Rytwinski et al. 2016) this reduction does not
Kristijn R.R. Swinnen et al. / Nature Conservation 47: 121–153 (2022)
reect a decrease in population but a decrease in wildlife victims due to the mitigation
measures. However, it might also reect a reduction in abundance of (a number of )
mammal species in Flanders that are most prone to being killed by vehicles.
Our species specic linear regression models indicate that 8 out of 17 mammal
species have a signicant change in proportion of roadkill victims through time. e
number of reported roadkill victims of Mustela putorius, the Western polecat, declines,
with the steepest signicant slope of all species (slope = -0.450). e polecat is sus-
pected to be in decline in Belgium, and also in most neighbouring countries (Croose
et al. 2018) and there are indications this decline was already present from 1998–2010
(Van Den Berge and Gouwy 2012).
e proportion of victims of the seven other species are increasing over the years.
Two species are (recently) recolonising (parts of) Flanders after a period of absence:
Eurasian beaver (Swinnen et al. 2017) and Wild boar (Rutten et al. 2019). Roe deer
has increased in range and numbers signicantly since the 70’s (Casaer and Huysen-
truyt 2016), Beech marten, is doing the same the last decades (Van Den Berge 2016)
and more recently, Badgers are also expanding from their last stronghold (Van Den
Berge et al. 2017). Although the increase in population density is not quantied, we
assume that this translates in higher relative roadkill numbers. e increase of the
Eurasian hare was unexpected since the species was recently added as vulnerable to the
red list of the Netherlands (bordering Flanders) (van Norren et al. 2020). However, for
Flanders no monitoring scheme is in place. For Wood mouse we have no knowledge
of population monitoring. is is a small-bodied species resulting in low carcass reten-
tion times (Santos et al. 2011; Ratton et al. 2014) and they were recorded relatively
infrequently indicating that these results have to be interpreted cautiously. Remark-
able is that the number of reported European hedgehog roadkill remains stable from
2010–2020. Until 2018, a strong decrease was occurring, but in 2019 and 2020 the
proportion abruptly increased and was again at the 2010 level. is increase is current-
ly unexplained but a fast recovery of the populations seems unlikely. ere are reports
of an unknown disease the last few years in Hedgehogs, possibly this also inuences
behaviour and making Hedgehogs more sensitive to being killed by cars.
Species distribution maps can be consulted at and addi-
tional info in Verkem et al. (2003). Linear regression models were also performed
for the period of 2010–2019 since the global pandemic of the coronavirus disease
(COVID-19) in 2020 resulted also in Flanders in connement measures which are
expected to have aected the search eort and the number of animals killed (Bíl et al.
2021; Driessen 2021). All trends remained similar, with the exception of the European
hare, where the increase became non-signicant.
Although the seasonal patterns are based on the rough data, without any correc-
tion for search eort within or between years, patterns of the same species are (highly)
consistent. We expect that the large amount of data smoothens smaller inter- and
within-year variation in search eort of individual observers. However, major events
are detectable. In Flanders, there was a strict ban on non-necessary (car)travel from the
18th of March 2020 to the 8th of June 2020 due to the COVID-19 pandemic. Apart
from the lives of wildlife this would have saved (Bíl et al. 2021; Driessen 2021), also
Animals under wheels 137
very few observers were on the road to quantify this eect. Determining which of both
factors was the most important is not possible using presence only data. For species in
which the peak period of kills overlaps with the connement measures, such as West-
ern polecat, the seasonal pattern of 2020 is clearly aected. Knowing the roadkill pat-
terns can help to protect specic species of interest by using specic warning signs, and
(temporal) road closure can even increase habitat quality (Whittington et al. 2019).
Although no age or sex of the individuals was recorded in most cases, most peaks in
roadkill density are presumed to be linked to increased movement because of mating or
juvenile movements and dispersal (Carvalho et al. 2018; Raymond et al. 2021).
We show that roadkill monitoring using citizen scientists can generate informa-
tive results. However, this is not the endpoint. Data collected during the ‘animals
under wheels’ project also contributed to the mitigation of local mortality hotspots.
Furthermore, the data can be consulted by policy makers and a number of questions
were asked in the Flemish Parliament concerning wildlife roadkill, indicating that the
problem is acknowledged at the political level.
Large quantities of roadkill records are collected by citizen scientists in Flanders, Bel-
gium. Volunteers remain engaged for a long period of time, probably due to the use of
a multi-purpose platform which also allows the registration of living organisms. Species
identication accuracy is high. Data collected using a standardised protocol is present,
however, data quantities are currently too low for nation-wide analysis. Currently, 94%
of all roadkill data are presence only records. Our results indicate that the amount of
mammal roadkill is diminishing in Flanders, possibly due to mitigation measures or
due to reduced population densities. We show that the citizen science data can be used
to detect trends in percentual abundance of roadkill per species per year and to show
seasonal patterns in relative roadkill density. Additional research to identify and conse-
quently mitigate roadkill hotspots, minimise and correct for biases and the comparison
between roadkill and population trends remains to be done. An increased eort to
convince observers to collect standardised transect data and photographs of roadkill
will increase the value of the dataset even further. We conclude that citizen scientists are
playing an important role in roadkill research and will continue to do so in the future.
e authors like to thank all citizen scientists for their records and the species experts for
the validation. Without your contributions, roadkill in Flanders would be a black box.
We thank Dominique Verbelen for his work on the bird species names. We thank the
IENE 2020 conference organising committee for the possibility to publish in the Special
issue: Linear Infrastructure Networks with Ecological Solutions. We thank the editor and
the anonymous referee for their contribution in signicantly improving the manuscript.
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Appendix A
For the 17 mammal species with more than 50 roadkill individuals, we show the linear
regression gures between year (2010–2020) and the percentual abundance per year.
Signicant regressions are shown with a black line, non-signicant with a grey line.
Figure A1.
Figure A2.
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Figure A3.
Figure A4.
Figure A5.
Kristijn R.R. Swinnen et al. / Nature Conservation 47: 121–153 (2022)
Figure A6.
Figure A7.
Figure A8.
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Figure A9.
Figure A10.
Figure A11.
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Figure A12.
Figure A13.
Figure A14.
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Figure A15.
Figure A16.
Figure A17.
Kristijn R.R. Swinnen et al. / Nature Conservation 47: 121–153 (2022)
Appendix B
For the 17 most recorded mammal species we show the variation in the roadkill pattern
within Flanders. For species with more than 1000 recordings, we show the pattern of
each individual year (2010-2020). For species with fewer than 1000 recordings all data
are combined to generate a general pattern.
Figure B1.
Figure B2.
Animals under wheels 149
Figure B3.
Figure B4.
Figure B5.
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Figure B6.
Figure B7.
Figure B8.
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Figure B10.
Figure B11.
Figure B9.
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Figure B12.
Figure B13.
Figure B14.
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Figure B15.
Figure B16.
Figure B17.
... The top 5 of roadkill animals in the present study (domestic cat, red fox, beech marten, European hedgehog and European badger) are species commonly found in other works. Research from the platform 'Animal under Wheels' (one of the largest roadkill database worldwide), published a top 10 of mammals roadkilled victims (Swinnen et al. 2022), with European hedgehog as the number one victim. Beech marten and red fox were placed at 5 and 6 place respectively. ...
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Road-killed animal surveys are scarce in Spain compared to other countries. Also, the majority of the published papers about this issue, comprehends data analysis from particular species or involve only motorways or highways. This paper presents the monitoring of a common road during three years, from 1 st January 2020 to 31 st December 2022 (both included), in order to evaluate wildlife loss by car hits. At the end of the survey, 53 individuals from 19 different species were found. The most affected groups were mammals with the 75% of car collisions and birds which obtained the 23% of total roadkills. Only one reptile was registered during the sampling. End of spring and summer were the seasons where wildlife was affected in high way, while autumn was the one with less fauna affection. The landscape across the road seems to influence on the biodiversity loss and according to conservation interests, most of the registered animals belongs to endangered or protected species, especially birds and reptiles. Also domestic animals like cats and dogs were directly affected by vehicle collisions. Annual results showed that roadkill data increased along the three years of survey, being 2020 the year with less incidences, probably due to the mobility restrictions established during the COVID-19 crisis.
... It is well known that anthropogenic structures and installations in wild areas directly, (e.g., roadkill, glass windows; Loss et al. 2014, Low et al. 2017, Marchesi et al. 2001, Guil and Pérez-García 2022, Swinnen et al. 2022) and indirectly (displacement by avoidance, behavioral adaptation, habitat fragmentation; Ibisch et al. 2016) affect wildlife populations. The two most common constructions in the wild, in addition to the ubiquitous urban sprawl, are roads and power lines (Ibisch et al. 2016, Martin et al. 2022. ...
Those requesting reprint please access till 6 Jan 2023 at the publishers for free Anthropogenic structures and installations in wild areas are known to directly and indirectly affect wildlife populations, especially apex predators such as Eagle Owls (Bubo bubo). To understand the situation at the national level we analyzed data collected by the Scientific Data Department of the Israel Nature and Parks Authority and the wildlife hospital at the Safari in Ramat Gan. We analyzed a total of 189 dead Eagle Owls during fifteen years, 2007-2021; 39.7% were electrocuted, 29.2% roadkill, 12.7% flew into fences/barbed wire, 3.8% drowned, and 14.9% died from other causes. The largest mortality of the Eagle Owls was detected in agricultural (34.92 %) and urban areas (31.74%). Also, the pylons identified as lethal should be prioritized and modified with appropriate insulators. Only a sincere effort on the part of the authorities will the continued electrocution of eagle owls and other avian wildlife be truncated.
... Data on road impacts obtained from citizen science projects can complement systematic surveys. Indeed, several citizen science projects have collected roadkill data and contributed to inform about the magnitude of road impacts on wildlife in different parts of the world (see Chyn et al., 2019;Périquet et al., 2018;Raymond et al., 2021;Swinnen et al., 2022;Valerio et al., 2021). Moreover, involving the public with the collection of roadkill data offers an opportunity to provide environmental education and awareness in local communities (Vercayie & Herremans, 2015), which could significantly contribute to reduce wildlife mortality on roads. ...
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Ecuador has both high richness and high endemism of species which are increasingly threatened by anthropic pressures, including roads. However, research evaluating the effects of roads remains scarce, making it difficult to develop mitigation plans. Here we present the first national assessment of wildlife mortality that allow us to 1) identify species and areas where mortality occurs due to collision with vehicles and 2) reveal knowledge gaps. We bring together data from systematic surveys and citizen science efforts in Ecuador to present a dataset with >5000 wildlife roadkill records from 454 species. Systematic surveys were reported by ten studies conducted in five out of the 24 Ecuadorian provinces. Collectively they revealed 282 species with mortality rates ranging from 0.008 to 95.56 ind./km/year. The highest rates were for the yellow warbler Setophaga petechia in Galápagos (95.56 ind./km/year), the cane toad Rhinella marina in Napo (16.91 ind./km/year), and the small ground-finch Geospiza fuliginosa in Galápagos (14.11 ind./km/year). Citizen science and other no systematic monitoring provided 1705 roadkill records representing all the 24 provinces of Ecuador and 299 species. The common opossum Didelphis marsupialis, the Andean white-eared opossum Didelphis pernigra, and the yellow warbler Setophaga petechia were more commonly reported (250, 104, and 81 individuals respectively). Across all sources, we found 15 species listed as Threatened and six as Data Deficient by the IUCN. We suggest stronger research efforts on areas where mortality of endemic or threatened species could be critical for populations, such as in Galápagos. This first assessment of wildlife mortality on Ecuadorian roads represents contributions from several sectors including academia, members of the public, and government underlining the value of wider engagement and collaboration. We hope these findings and the compiled dataset will guide sustainable planning of infrastructure in Ecuador and ultimately, contribute to reduce wildlife mortality on roads.
... Data were collected from the '' database, an online citizen science platform that contains both semi-structured and opportunistic records (Swinnen et al., 2022). The selected dataset included year-round records from January 2014 to September 2019 with a geographical precision of 500 metres or smaller (records can be submitted to the platform as point locations with specified precision or as observations made within a larger area). ...
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Opportunistic citizen science data are commonly filtered in an attempt to improve their applicability for relating species occurrences with environmental variables. Recommendations on when and how to filter, however, have remained relatively general and associations between species traits and filtering recommendations are sparse. We collected six traits (body size, detectability, classification error rate, familiarity, reporting probability and range size) of 52 birds, 25 butterflies and 14 dragonflies. Both absolute (values not rescaled) and relative traits (values rescaled per taxonomic group) were linked to filter effects, i.e. the impact on three different measures of species distribution model performance caused by applying three different quality filters, for different degrees of sample size reduction. First, we applied multiple regressions that predicted the filter effects by either absolute (including taxonomic group) or relative traits. Second, a principal component and clustering analysis were performed to define five species profiles based on species traits that were retained after a multiple regression model selection. The analysis of the profiles indicated the relative importance of species traits and revealed new insights into the association of species traits with changes in model performance after data quality filtering. Both taxonomic group (more than absolute traits) and relative species traits (mainly classification error rate, range size and familiarity) defined the impact of data quality filtering on model performance and we discourage the selection of a quality filtering strategy based on one single species trait. Results further confirmed the importance of considering the goal of the study (i.e. increasing model discrimination capacity, sensitivity or specificity) as well as the change in sample size caused by stringent filtering. The general species knowledge amongst citizen scientists (importance of observer experience), together with the mechanism of record verification in an opportunistic data platform (importance of verifiable metadata) have the largest potential for enhancing the quality of opportunistic records.
Conference Paper
Infrastructure projects can be the source of multiple environmental impacts, such as roadkill, habitat loss, and habitat fragmentation. Specialty studies have shown that almost 194 million birds and 29 million mammals may be killed each year on European roads. In our country, the dense network of national roads often causes roadkill events, with the following possible impact forms: reduction of wildlife species populations, damage to population fitness, changes in the spatial distribution and migration routes. At the same time, the presence of an animal on the road can also lead to a degree of discomfort and endangerment for traffic participants. At the same time, highways have their fair share of roadkill events. In Romania, the highway network includes only about 961 km built, the investments in the field of infrastructure being made mainly to connect the east (the Black Sea coast) with the west (the connection with other countries from the European Union). Nowadays, highway networks are being built to become mobility lanes to other regions of the country and exits to other neighboring countries. Recently, there is an increased focus on the environmental component for large infrastructure projects at national level, with solutions that can be implemented from the feasibility study phase. The paper deals with wildlife roadkill findings, associated with different infrastructure projects throughout the country, correlated with the proposal of measures to stop or reduce such negative events.
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Wildlife-vehicle collisions are one of the main causes of mortality for wild mammals and birds in the UK. Here, using a dataset of 54,000+ records collated by a citizen science roadkill recording scheme between 2014–2019, we analyse and present temporal patterns of wildlife roadkill of the 19 most commonly reported taxa in the UK (84% of all reported roadkill). Most taxa (13 out of 19) showed significant and consistent seasonal variations in road mortality and fitted one of two seasonal patterns; bimodal or unimodal: only three species (red fox Vulpes vulpes , European polecat Mustela putorius and Reeves’ muntjac deer Muntiacus reevesi ) showed no significant seasonality. Species that increase movement in spring and autumn potentially have bimodal patterns in roadkill due to the increase in mate-searching and juvenile dispersal during these respective time periods (e.g. European badger Meles meles ). Unimodal patterns likely represent increased mortality due to a single short pulse in activity associated with breeding (e.g. birds) or foraging (e.g. grey squirrels Sciurus carolinensis in autumn). Importantly, these patterns also indicate periods of increased risk for drivers, potentially posing a greater threat to human welfare. In addition to behaviour-driven annual patterns, abiotic factors (temperature and rainfall) explained some variance in roadkill. Notably, high rainfall was associated with decreased observations of two bird taxa (gulls and Eurasian magpies Pica pica ) and European rabbit Oryctolagus cuniculus . By quantifying seasonal patterns in roadkill, we highlight a significant anthropogenic impact on wild species, which is important in relation to conservation, animal welfare, and human safety.
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Millions of wild animals are killed annually on roads worldwide. During spring 2020, the volume of road traffic was reduced globally as a consequence of the COVID-19 pandemic. We gathered data on wildlife-vehicle collisions (WVC) from Czechia, Estonia, Finland, Hungary, Israel, Norway, Slovenia, Spain, Sweden, and for Scotland and England within the United Kingdom. In all studied countries WVC statistics tend to be dominated by large mammals (various deer species and wild boar), while information on smaller mammals as well as birds are less well recorded. The expected number of WVC for 2020 was predicted on the basis of 2015–2019 WVC time series representing expected WVC numbers under normal traffic conditions. Then, the forecasted and reported WVC data were compared. The results indicate varying levels of WVC decrease between countries during the COVID-19 related traffic flow reduction (CRTR). While no significant change was determined in Sweden, where the state-wide response to COVID-19 was the least intensive, a decrease as marked as 37.4% was identified in Estonia. The greatest WVC decrease, more than 40%, was determined during the first weeks of CRTR for Estonia, Spain, Israel, and Czechia. Measures taken during spring 2020 allowed the survival of large numbers of wild animals which would have been killed under normal traffic conditions. The significant effects of even just a few weeks of reduced traffic, help to highlight the negative impacts of roads on wildlife mortality and the need to boost global efforts of wildlife conservation, including systematic gathering of roadkill data.
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Opportunistically collected species occurrence data are often used for species distribution models (SDMs) when high-quality data collected through standardized recording protocols are unavailable. While opportunistic data are abundant, uncertainty is usually high, e.g. due to observer effects or a lack of metadata. To increase data quality and improve model performance, we filtered species records based on record attributes that provide information on the observation process or post-entry data validation. Data filtering does not only increase the quality of species records, it simultaneously reduces sample size, a trade-off that remains relatively unexplored. By controlling for sample size in a dataset of 255 species, we were able to explore the combined impact of data quality and sample size on model performance. We applied three data quality filters based on observers' activity, the validation status of a record in the database and the detail of a submitted record, and analyzed changes in AUC, Sensitivity and Specificity using Maxent with and without filtering. The impact of stringent filtering on model performance depended on (1) the quality of the filtered data: records validated as correct and more detailed records lead to higher model performance, (2) the proportional reduction in sample size caused by filtering and the remaining absolute sample size: filters causing small reductions that lead to sample sizes of more than 100 presences generally benefitted model performance and (3) the taxonomic group: plant and dragonfly models benefitted more from data quality filtering compared to bird and butterfly models. Our results also indicate that recommendations for quality filtering depend on the goal of the study, e.g. increasing Sensitivity and/or Specificity. Further research must identify what drives species' sensitivity to data quality. Nonetheless, our study confirms that large quantities of volunteer generated and opportunistically collected data can make a valuable contribution to ecological research and species conservation.
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Globally, wildlife-vehicle conflict (WVC) fragments wildlife populations (due to road/traffic-aversion), kills and injures individual animals, can cause wildlife population declines, may eventually contribute to local or total extinction of certain species, and can harm vehicles and drivers. Preventing WVC begins with recording locations of conflict, such as vehicle crashes, animal carcasses (roadkill), or animal behavior around roads, such as avoidance of roads or crossing-behavior. These data are ideally used to inform transportation policy and planning and to retrofit roadways and their structures to reduce WVC. We are collectively involved with or manage eight regional or national systems for reporting WVC in collaboration with volunteers and/or agency staff. In this review, we survey systems for recording WVC by volunteers and agency staff at different geographical scales, based on existing literature and our personal experience. We report the range of data collection methods, data management systems and data visualizations employed as well as discuss the groups and type of volunteers and agencies involved. We use our expertise and the global survey to provide methodological specifications based on current best-practice for collecting and using WVC data to inform transportation and conservation decisions. We conclude with a vision of next steps toward a global network of WVC reporting systems, that have clear and practical applications for improved conservation research as well as guidelines for management of road networks.
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Australia has no national roadkill monitoring scheme. To address this gap in knowledge, a roadkill reporting application (app) was developed to allow members of the public to join professional researchers in gathering Australian data. The app is used to photograph roadkill and simultaneously records the GPS location, time and date. These data are uploaded immediately to a website for data management. To illustrate the capacity to facilitate cost-effective mitigation measures the article focuses on two roadkill hotspots-in Queensland and Tasmania. In total, 1609 reports were gathered in the first three months of the project. They include data on mammals (n = 1203, 75%), birds (n = 125, 7.8%), reptiles (n = 79, 4.9%), amphibians (n = 4, 0.025%), unidentified (n = 189, 11.8%) and unserviceable ones (n = 9). A significant finding is variance in the distribution of mammals and birds at different times of day. These findings reflect diurnal variation in the activity levels of different species and underline the need for data on a targeted species to be collected at appropriate times of day. By continuing to facilitate roadkill monitoring, it is anticipated that the data generated by the app will directly increase knowledge of roadkill numbers and hotspots. Indirectly, it will provide value-added information on animal behaviour, disease and population dynamics as well as for species distribution mapping.
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Road vehicle collisions are likely to be an important contributory factor in the decline of the European hedgehog ( Erinaceus europaeus) in Britain. Here, a collaborative roadkill dataset collected from multiple projects across Britain was used to assess when, where and why hedgehog roadkill are more likely to occur. Seasonal trends were assessed using a Generalized Additive Model. There were few casualties in winter—the hibernation season for hedgehogs—with a gradual increase from February that reached a peak in July before declining thereafter. A sequential multi-level Habitat Suitability Modelling (HSM) framework was then used to identify areas showing a high probability of hedgehog roadkill occurrence throughout the entire British road network (∼400,000 km) based on multi-scale environmental determinants. The HSM predicted that grassland and urban habitat coverage were important in predicting the probability of roadkill at a national scale. Probabilities peaked at approximately 50% urban cover at a one km scale and increased linearly with grassland cover (improved and rough grassland). Areas predicted to experience high probabilities of hedgehog roadkill occurrence were therefore in urban and suburban environments, that is, where a mix of urban and grassland habitats occur. These areas covered 9% of the total British road network. In combination with information on the frequency with which particular locations have hedgehog road casualties, the framework can help to identify priority areas for mitigation measures.
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Daily, a large number of animals are killed on European roads due to collisions with vehicles. A high proportion of these events, however, are not documented, as those obliged to collect such data, only record a small proportion; the police only register collisions that lead to traffic accidents, and hunters only collect data on game wildlife. Such reports disproportionately under-records small vertebrates such as birds, small mammals, amphibians and reptiles. In the last decade, however, national wildlife roadkill reporting systems have been launched, largely working with citizen scientists to collect roadkill data on a national basis that could fill this data gap. The aim of this study is, therefore, to describe for the first time, existing projects in Europe, and the user groups that submit data to them. To give a deeper understanding of such projects, we describe exemplar scientific roadkill reporting systems that currently exist in Austria, Belgium, Czechia and the United Kingdom. We define groups of people who contribute to such citizen science activities, and report our experience and best practice with these volunteers. We conclude that volunteers contribute significantly to collecting data on species that are not typically recorded in official databases. To ensure citizen-science projects perpetuate, (I) volunteers need to be motivated by the organisers to participate on a long-term basis, (II) volunteers need support in identifying roadkill species where required, and (III) regular feedback is required on how their contribution is used to produce new scientific knowledge.
The COVID-19 pandemic provides a rare opportunity to reveal the impact of reduced human activity on wildlife. I compared traffic volume and wildlife roadkill data along 18 km of highway before, during and after a 3-month period of COVID-19 restrictions with baseline data from the previous four years. Three marsupial herbivores comprised 89% of the 1820 roadkills recorded during the 4.5-year survey period: rufous-bellied pademelon Thylogale billardierii (31.5% of total), common brushtail possum Trichosurus vulpecula (29.8%) and red-necked wallaby Notamacropus rufogriseus (27.9%). During April 2020, when human activity was most restricted in the study area, traffic volume decreased by 36% (i.e. by an average 13,520 vehicle movements per day) and wildlife roadkill decreased by 48% (i.e. from 44 to 23 roadkills). However, when restrictions eased, traffic volume and wildlife roadkill returned to baseline levels indicating that the respite was brief in terms of animal welfare and of limited conservation value for these widespread and abundant species. Nevertheless, the results of this study suggest that even short periods of traffic reduction or road closures could be used as part of a management strategy for the conservation of endangered wildlife populations and re-wildling programs where roadkill is a risk factor.
Animal-vehicle collisions (AVCs) involving ungulate species pose a serious problem in many countries, and the prediction of accidents and costs on a regional and national spatial scale is important for efficient accident mitigation. Based on the assumption that AVCs are determined by traffic volume and ungulate population dynamics, this study developed a relatively simple method for calculating and predicting the costs of current and future traffic accidents involving moose, roe deer and wild boar in Sweden. A logistic population model was assumed for all three ungulate species and econometric methods were applied to obtain population growth models based on panel data on traffic accidents, traffic load, hunting bags, hunting licenses and landscape characteristics for each Swedish county and year from 2003 to 2015. The population growth models were used to predict vehicle accidents and costs. The predicted annual discounted costs of AVCs over a 15-year period based on projected ungulate populations and traffic volume fell by 25% from 406 million USD in 2015, however the allocation of costs between ungulates differed. AVCs involving roe deer accounted for the largest share of the costs (54%), but collisions involving wild boar showed the most rapid increase over the study period because of a relatively high estimated growth rate and recent expansion of wild boar populations to several new counties. However, the predicted costs were sensitive to assumptions regarding population dynamics as well as assumptions about future hunting pressure and traffic volume.
Roads represent a threat to biodiversity, primarily through increased mortality from collisions with vehicles. Although estimating roadkill rates is an important first step, how roads affect long‐term population persistence must also be assessed. We developed a trait‐based model to predict roadkill rates for terrestrial bird and mammalian species in Europe and used a generalized population model to estimate their long‐term vulnerability to road mortality. We found that ~194 million birds and ~29 million mammals may be killed each year on European roads. The species that were predicted to experience the highest mortality rates due to roads were not necessarily the same as those whose long‐term persistence was most vulnerable to road mortality. When evaluating which species or areas could be most affected by road development projects, failure to consider how roadkill affects populations may result in misidentifying appropriate targets for mitigation.