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The Impact of Roadkill on Cervid Populations in Lithuania

Nature Research Centre, Akademijos 2, 08412 Vilnius, Lithuania
European Commission, Joint Research Centre, Via Fermi 2749, 21027 Ispra, Italy
Author to whom correspondence should be addressed.
Forests 2023, 14(6), 1224;
Original submission received: 19 April 2023 / Revised: 26 May 2023 / Accepted: 10 June 2023 / Published: 13 June 2023
(This article belongs to the Special Issue Conservation and Management of Forest Wildlife)


Cervid roadkill, including moose, red deer and roe deer, can pose a risk to drivers and are frequently registered. However, the roadkill influence on overall cervid populations is not fully known, especially by roadkill that are not officially registered. The aim of this study was to assess the impact of cervid roadkill on population abundance, evaluating (i) the proportion of ungulate–vehicle collisions not registered by official bodies, (ii) the number of roadkill in relation to hunted animals and (iii) the proportion of roadkill that occurs in forest habitat. The number of unreported roadkill was calculated based on a roadkill index assessed during 3815 registration sessions on main and national roads in Lithuania from 2002–2022. During this period, 373 moose, 712 red deer and 9179 roe deer roadkill were unreported, correlating to 13.8%, 95.8% and 31.1% of those registered by the Traffic Supervision Service. In conclusion, 39.5%, 17.5% and 20.1% of roadkill were registered on roads through forests. Moose roadkill amounted to a figure corresponding to 10% of those hunted, with the same figure for red deer being 1.8%, neither adding much mortality. At 16.5%–16.6%, the figure for roe deer might be important.

1. Introduction

Moose (Alces alces), red deer (Cervus elaphus) and roe deer (Capreolus capreolus) are forest-related ungulates of the middle latitudes [1,2,3]. Due to current tendencies of ungulate overabundance in European landscapes [4,5], their role and influence in forest ecology are being debated [6,7]. The abovementioned authors show that ungulates indirectly increase herbaceous richness but harm forest plant communities, leading to biotic homogenization; thus, even at low density, ungulates are a conservation concern for plant composition. Even being forest-related species, A. alces and C. elaphus use forest gaps [8], but they tend to use the matrix of the landscape surrounding forests [9]. C. capreolus uses field habitats most often and this species even forms two ecotypes—the forest roe deer and field roe deer [10].
The forest is not only the main habitat of these ungulate species, but, unfortunately, it is related to the phenomenon of ungulate roadkill [11]. In investigations into ungulate roadkill patterns, forests were characterized as very strong environmental factors [12,13,14], being related to the spatial distribution and densities of roadkill. As one of the predictor habitats, forest presence is used as a determinant factor in roadkill modeling [15,16].
In Europe at least, ungulate–vehicle collisions (UVCs) are one of the consequences of the high abundance of ungulates [4]. With increasing population and associated roadkill in recent decades [17], not only does this have consequences for road safety, but it can also be a factor in the population dynamics of the cervids. This, however, is not fully understood and generally has not been utilized in population assessments and management [18], the usual consideration of the main ungulate mortality factors being hunting [19,20] and landscape changes [21].
The increase in UVC numbers in Europe, and the associated risks to road safety and the effect on animal numbers, have been and still are recognized in some works [17,22]. UVCs have been identified and analyzed in many European countries. As shown below, in many countries, roadkill of cervids is the most frequent and the most dangerous. However, in some western countries, such as Belgium, France, Spain and Portugal, the most common roadkilled ungulate is S. scrofa.
In the Nordic countries, namely Norway, Sweden and Finland, the main emphasis has been on A. alces and C. elaphus [23,24,25,26], the interest having been sustained by the higher risks of such collisions to humans. Cervids have been the main species of interest in England [27,28], but roadkill of smaller wildlife species, such as otters (Lutra lutra), badgers (Meles meles) and hedgehogs (Erinaceus europaeus), have also been analyzed [29,30,31].
In Belgium, S. scrofa roadkill dominate UVCs, while roadkill of C. capreolus and C. elaphus are less numerous [32,33]. In France, roadkill of S. scrofa and C. capreolus dominate, resulting in extremely large economic losses—it was calculated that the presence of wolves and their consumption of ungulates saves 2.4–7.8 million euros by preventing UVCs [34]. Sus scrofa also dominates UVCs in Spain [35,36,37,38] and Portugal [17], C. capreolus being much less involved.
Long-term ungulate roadkill in Czechia is dominated by C. capreolus and S. scrofa, comprising 75% and 15%, respectively [39]. In this country, the highest level of mortality was found in C. capreolus, with annual losses of this species estimated at more than one hundred thousand individuals, while the annual roadkill of the bigger ungulate C. elaphus totals a few thousand individuals [40]. These three herbivore species are responsible for the most animal–vehicle collisions, comprising 63.8%, 30.1% and 3.1% of collisions, respectively [41]. UVCs are strongly related to the annual average daily traffic [42].
In the Balkan countries, the situation with UVCs is similar. In Slovenia, the ungulates prevailing in roadkill are C. capreolus (3600–4800 annual toll), followed by C. elaphus (91–116 individuals) and S. scrofa (19–62 individuals). A higher risk of UVC occurs in fragmented landscapes abounding at forest edges [43]. Similarly, in Croatia, registered UVC cases are distributed as follows: 70.1% of roadkill are C. capreolus, 11.0% are S. scrofa, 4.8% are fallow deer (Dama dama) and only 0.9% are cases of C. elaphus [44].
In the Baltic countries and neighboring Poland, the issue of UVCs has been investigated differently. We did not find publications on animal–vehicle collision analysis in Latvia, though the number of UVCs was indicated to be 3190 in 2017 [45]. In Estonia, only S. scrofa-based UVCs were analyzed for temporal patterns [46]. In Lithuania, most of the publications concerning animal–vehicle collisions were produced by the authors of this paper [11,19,20,47,48,49,50,51,52,53,54,55], covering species composition, methodological, temporal and habitat-related roadkill aspects and the planning of mitigation measures. In Poland, most publications analyze various aspects of wild animals in general [56,57,58], including the influence of the COVID lockdown [59], while only a few are targeted specifically to A. alces or to all cervid species [60,61].
In general, the importance of animal–vehicle collisions across Europe is recognized as an important factor [45]. It might have a negative influence on biological diversity [62] and is dependent on habitat connectivity issues, including those in protected areas [63]. Therefore, knowledge regarding the spatial characteristics of roadkill, especially their concentration sites (hotspots), is required for planning mitigation measures [55].
In the above-mentioned investigations of UVCs, the issue of registration efficiency was not accounted for, though the importance of it cannot be denied [64,65,66]. Registration efficiency can be evaluated using professionally collected [53] or citizen-science [64,65] data, but most investigations (e.g., [28,33,39], etc.) rely solely on official registration databases. Our previous study [53] indicated the presence of unreported ungulate roadkill in Lithuania, but their influence on the population dynamics of A. alces and C. elaphus were not discussed [19,20].
Therefore, the aim of this study is to assess the impact of roadkill of forest cervids (A. alces, C. elaphus and C. capreolus) on their populations in Lithuania, evaluating the proportion of unreported UVCs and the number of UVC roadkill in relation to the number of hunted animals. We also evaluated the proportion of roadkill in forest habitats.

2. Materials and Methods

2.1. Study Site

Our study was conducted in Lithuania, a flat country of northern Europe bordering the Baltic Sea (Figure 1). The country surface is a mosaic: 33% is covered by forests, another 33% is occupied by arable land and permanent crops, 27% by semi-natural vegetation, 3% by artificial areas and 4% by water bodies and other land [67]. Of the 68 species of mammals that occur in Lithuania, eight of these are ungulates [68].
The state-owned road network in Lithuania is 21,249 km long (Figure 1) and includes main roads (total length 1750 km with annual average daily traffic being 3000–20,000 cars per day), national roads (4296 km, 500–3000 cars per day) and regional roads (14,574 km, up to 500 cars per day) [69].
The main UVC mitigation measures in the country are wildlife fences, used in combination with underground wildlife passages, safety gates, jump-outs and open passages at ground level [55]. The total length of fences in the country is 615.2 km, of which 262.0 km are in forests [70]. The average length of the fenced road sections is longer in forests than outside forests, comprising 506.9 and 348.6 m, respectively.

2.2. Data Collection

2.2.1. Lithuanian Police Traffic Supervision Service Data

Reporting collisions with an animal to the authorities is not mandatory in Lithuania if there are no injuries to people or damage to vehicles or road infrastructure and if there are no insurance issues. However, UVCs are usually reported and registered by the Lithuanian Police Traffic Supervision Service, as these collisions result in damage. To get car insurance money, the collision with the animal must be confirmed by a Traffic Supervision Service report. Therefore, the main source of UVC data is from registered roadkill, which provides the number of cervids killed in UVCs.
When the driver reports the collision, representatives of the Traffic Supervision Service are informed. They arrive at the scene and fill in a report form. The date, location of collision and species name are entered. In the past, the location was determined by road number and kilometer (precision 100 m), but more recently by coordinates (GPS).

2.2.2. Nature Research Centre Data

The second source is a database of roadkill not reported to the Traffic Supervision Service, but found by the professional roadkill registration carried out at the Nature Research Centre [53]. This database was created by the authors and is held by the Nature Research Centre.
Professional roadkill reporting was performed by biologists randomly driving main, national and sometimes regional roads. One route on one same-numbered road on the same day was termed a registration session. Data were collected over 3815 sessions in 2002–2022, with the total length of road being 294,440 km. The average distance, covered in one registration session, was 77.2 ± 1.0 km, with a minimum of 4.5 km and a maximum of 760 km. The number of registration sessions was from 23 to 509 for the main roads and from 1 to 41 for the national roads. Registrations were performed all year round. In addition to the above information, the professional roadkill registration investigator also records registration efforts, that is, the length of the route as an indirect measure, for the further calculation of the roadkill index. The Nature Research Centre is the only organization carrying out professional registration of roadkill in Lithuania. The species of cervids were identified in all UVCs found by professionals [53].
To be sure roadkill was not reported, it was checked against the data of the Traffic Supervision Service, comparing species, date and coordinates. Further in the paper, we will refer to these cases as “unreported”.
While the first source gives numbers of cervids killed in UVCs, the second allows recalculation of the unreported UVC numbers based on registration efforts. Covering 2002–2022, we used data on UVCs from both sources. We analyzed 50,679 roadkill; 32,944 of these were UVCs, including 32,707 collisions with the cervids A. alces, C. elaphus and C. capreolus. In 5874 cases of reported roadkill, the animal species was not identified, which may mean there were additional individuals of the species of interest who were killed but that cannot be accounted for. This was due to the lack of training of Lithuanian Police Traffic Supervision Service officers.
All the roadkill records from the police and from the Nature Research Centre were georeferenced to be able to identify the habitat at the site of collision. Habitat was categorized as either forested or not forested.

2.2.3. Cervid Population Size and Hunting Bag

Data of surveyed cervid numbers from the last decade, including EV, are available at (accessed on 10 April 2023) and data on the hunting bag at (accessed on 10 April 2023). Data on the BV are from the author databases; they were obtained from the archives of the Ministry of the Environment of the Republic of Lithuania.
As we wrote earlier in [20], “Data on the population size and the number of hunted animals up to the 1990s were reported in the areas of forest enterprises and later in the areas of the hunting clubs. Snow-track counts with correction by the number of visually registered animals was the main method. Number of hunted deer is the sum of used permits every hunting season, as hunters are obliged to report every kill”.

2.3. Statistical Analyses

To estimate the number of cervids that were killed on the road but not recorded by the Police Traffic Supervision Service, we calculated a roadkill index, RI = kc/L, expressed as the number of roadkilled cervids per km. The roadkill index used the number of cervids killed in road collisions recorded by the Nature Research Centre divided by the total distance monitored in kilometers for each monitoring session. The RI was calculated for every session and is species-specific. No duplicates with data from the Traffic Supervision Service were allowed.
Later, we extrapolated these findings to estimate additional roadkill that was unreported. In using the roadkill index, we assume that the sampling sessions conducted by the Nature Reserve Centre were random and reflect the actual number of unreported cervids killed on roads independent of habitat structure.
However, most of the professional registration sessions, 99.05%, were conducted on main and national roads, while regional roads were represented by a mere 2790.33 km (0.95%). To accommodate the uneven sampling effort, we adjusted the recalculation to include main and national roads only. The number of non-reported roadkill every year was re-calculated as
RI × 365 × (1750 + 4926),
where 1750 is the length of main roads and 4926 is the length of national roads in km, and regional roads are not included. Possible bias of this adjustment is presented in the Section 5.
The recalculation of unreported data allows for an estimation of the full impact of roadkill as the sum of both registered and unreported cases. This is the only way (apart from citizen science, which has no interest in this issue in Lithuania) to supplement the data available to the Traffic Supervision Service.
To analyze the long-term trends of cervid numbers, roadkill and hunting bags (annual numbers of hunted individuals) separately, we calculated the compound annual growth rate (CAGR). The CAGR shows a smoothed increase or decrease over a given period.
Twenty-one years (2002 to 2022) of cervid numbers and roadkill were included, and 20 years of hunting bag data were included, as the 2022/2023 data were not available at the time of writing.
Using the CAGR, we assessed the average annual growth rate over a specified period, which helped in understanding the pace of population change; however, we did not make projections for the future. The CAGR was calculated as
CAGR = (EV/BV)^1/n − 1,
where EV is the final value (number of animals present, hunted or roadkilled in the last year), BV is the initial value (number of animals present, hunted or roadkilled in the first year) and n is the number of years.
Trends were approximated as linear or exponential, and significance was evaluated by R2 and p in the first case and by Akaike’s IC in the second. Calculations were conducted in PAST, version 4.05 (Ø. Hammer, D.A.T. Harper, Oslo, Norway).
The annual statistics of the RI, including standard error (SE), were calculated from all registration routes of that year, including all “empty” ones, where no cervid roadkill were found. The proportions of cervid roadkill in forest and non-forest habitats, as well as the proportion of roadkill in fenced and non-fenced areas, were presented as the mean with a 95% confidence interval (CI) and compared using an online calculator [71] and the G test. Effect size was expressed using Cohen’s w. The CAGR was calculated in Excel, according to the formula in [72]. Other calculations were performed in Statistica for Windows, version 6.0 (StatSoft, Inc., Tulsa, OK, USA).

3. Results

3.1. Registered and Unreported Roadkill of Cervids

The Lithuanian Police Traffic Surveillance Service registered 2705 collisions with A. alces; 3 were unreported. The corresponding figures for C. elaphus were 736 and 7, and for C. capreolus, 29,199 collisions were registered and 57 were unreported, as the roadkill were found by professional biologists.
Annual RI values are presented in Table 1. As shown by standard error, there were single cases of unreported roadkill of A. alces and C. elaphus most years.
Between 2002–2022, the number of roadkilled A. alces registered by the Traffic Supervision Service was 2708, that of C. elaphus was 743 and that of C. capreolus was 29,256. The occurrence of unreported roadkill was sporadic in A. alces and C. elaphus but constantly present in C. capreolus (Figure 2).
Speaking about trends, there was a linear increase of registered A. alces roadkill numbers from 19 to 295 individuals per year in 2002–2019, which then stabilized at about 250 individuals (Figure 2a). This linear model is highly significant (R2 = 0.92, p < 0.0001). C. elaphus roadkill were low in 2002–2013, being 5–16 individuals per year, but numbers then increased and stabilized at about 100–120 individuals (Figure 2b). The trend is best described as exponential (Akaike’s IC = 3356.9). An exponential increase of registered C. capreolus roadkill numbers, from 150 in 2002 to nearly 4000 individuals in 2020 (Figure 2c), was also significant (Akaike’s IC = 2,231,400).
Based on the unreported data, 373 roadkill of A. alces were not registered by officials during the investigation time, with a peak number in 2016 (Figure 2a) equaling 13.8% of the registered roadkill. The number of not officially registered roadkilled C. elaphus was 712 during 2002–2022, with a peak number in 2017 (Figure 2b) and with the number of unreported roadkill being equal to the registered roadkill. The number of not officially registered roadkill of C. capreolus was the highest, this being 9179 individuals (Figure 2c), equaling 31.1% of roadkill in the database of the Traffic Supervision Service.

3.2. Roadkill Relation to Forest Habitat

It was found that on average, 39.5% (95% CI = 37.6%–41.4%) of A. alces, 17.5% (14.8%–20.4%) of C. elaphus and 20.1% (19.7%–20.6%) of C. capreolus roadkill were registered on roads going through forest habitat, while the rest of the roadkill of these species occurred out of the forest (Table 2). Based on the G-test, the proportions differed significantly (G = 470.2, p < 0.0001), with A. alces roadkill being the most forest-related.
While the proportion of C. elaphus roadkill in forest habitats remained stable from 2002–2022, those of A. alces and C. capreolus indicated a decreasing trend (Table 2). The proportion of the forest-related roadkill of A. alces decreased from about 50%–60% during 2002–2011 to 35%–40% after 2015. The linear trend explains a significant part of the data variability (R2 = 0.67, p < 0.001). The proportion of the forest-related roadkill of C. capreolus decreased from about 30% in 2002–2007 to 20% after 2015. The linear trend explains more than half of the data variability and is significant (R2 = 0.54, p < 0.0001).
A higher number of A. alces roadkill in forests were registered on the main and national roads in the eastern and southeastern parts of the country, where the forest percentage is higher. Outside forests, A. alces were frequently roadkilled in central and southwestern parts characterized as agricultural areas (Figure 3). Low numbers of C. elaphus roadkill in the forests had no expressed spatial distribution, except for being related to main roads (Figure 4). Forest-related roadkill of C. capreolus were concentrated in the biggest forest areas (Figure 5).
We also compared the number of roadkill on fenced and unfenced sections of roads in forest and non-forest habitats. On average, 4.2% (CI = 4.0%–4.4%) of cervid roadkill were registered on fenced roads. In forest habitats this proportion was 8.1% (CI = 7.6%–8.7%) and in open habitats it was 3.0% (CI = 2.8%–3.2%). This proportion is significantly higher (G = 306.7, p < 0.0001); however, it has limited importance (w = 0.106, small effect size).
For all three cervid species, a similar trend was observed, with a higher proportion of deer killed on fenced forest roads compared to fenced road sections in open habitats. The proportion of A. alces roadkill was 11.3% (CI = 9.5–13.5) on fenced roads in forest habitats compared to 8.6% (CI = 7.2–10.1) on fenced road sections in open areas. The first proportion is significantly higher (G = 5.4, p < 0.05). Respective proportions of C. elaphus roadkill were 12.4% (CI = 7.3–19.4) vs. 5.1% (CI = 3.5–7.1); the difference is significant (G = 7.1. p < 0.01) and the effect size is small (w = 0.114). The proportion of C. capreolus roadkilled on the fenced road segments in the forest was 7.4% (CI = 6.8–8.1), compared to 2.6% (CI = 2.4–2.8) in open areas, the first one being significantly higher (G = 2693.8, p < 0.0001). The effect size, however, is also small (w = 0.106).

3.3. Comparison of Cervid Roadkill and Hunting Bag

The long-term trend of the A. alces hunting bag is linearly increasing and significant (Figure 6). This corresponds with an increase in population numbers from 3860 in 2004 to 20,676 in 2022. However, the average hunting bag in 2002–2022 was just 7.6% of the surveyed numbers. It evolved from zero (hunting ban) in 2004, thereafter slowly increasing to 2%–4% until 2010, and then steadily increasing afterwards to 13%–14% in 2018–2021.
The CAGR value of A. alces numbers for the period 2002–2022 was 0.0758, that of the hunting bag 0.0879 and that of the roadkill 0.1308. Despite the slower long-term trend of population increase compared to the increase of hunting bags, the A. alces population is still expanding. The proportion of animals killed on the road in relation to the number of animals hunted is decreasing. The long-term trend, however, is not significant (p = 0.20). During years of limited hunting, the proportion of roadkilled animals was very high, increasing from 28.6% in 2005 to 78.8% in 2009 (Figure 6). Afterwards, it gradually decreased to 25.6% in 2014. In 2018–2021, the proportion of roadkilled A. alces compared to hunted numbers was 8.7%–11.3%. Thus, roadkill should only be considered an important factor in the population management in years of low population size and limited hunting.
At 9.8%, the long-term average hunting bag of C. elaphus was also low. From zero (hunting ceased) in 2004, it increased steadily to 18.1% of the surveyed population in 2021. This long-term trend is linear and significant (p < 0.001). The CAGR value for the period 2002–2022 was 0.0968, the hunting bag value was 0.1202 and the roadkill value was 0.1867. Despite the slower long-term trend of population increase compared to the increase of hunting bags, the population increased from 10,584 individuals in 2003 to 77,300 individuals in 2021. Although the number of C. elaphus roadkill has increased at the fastest rate, the proportion of roadkilled animals relative to hunted animals remains the same, with no trend (Figure 6). Compared to an average of 1.8%, the few peaks in 2009, 2014 and 2017 equaled only 6%–7% of the number of animals hunted, so roadkill have not been a significant factor in C. elaphus mortality.
The above-mentioned patterns in C. capreolus were different. The average hunting rate between 2002 and 2021 was 16.7%, with no significant trend, except for a decrease to 12%–14% in 2011–2014 and 13.1% in 2020 (Figure 6). Even with a very low CAGR of 0.0444, the number of C. capreolus increased from 62,276 in 2002 to 172,599 in 2022. The CAGR of the hunting bag was even less, 0.0417; expressed in raw numbers, it increased from 11,569 in 2002 to 26,206 in 2021. The rate of roadkill of C. capreolus increased most rapidly, with the CAGR being 0.1619, resulting in 3–5 thousand individuals being killed annually on the roads between 2016 and 2022. The trend of this increase is linear and significant (p < 0.001). As a result, whereas the number of roadkilled individuals equaled 1.3% of the number hunted in 2002, this increased to 16.5%–16.6% in 2020–2021. The long-term average for this share is 7.7%.

4. Discussion

Our analyses show that the total number of roadkill of A. alces in Lithuania increased linearly from 2002–2022, while the numbers of roadkilled C. capreolus from 2014–2020 increased exponentially. Those of C. elaphus stabilized after 2013.
We also found that during 2002–2022, 13.8% of A. alces and 31.1% of C. capreolus roadkill were not reported, the latter species adding over 9000 roadkilled individuals. Numbers of not-reported roadkill of C. elaphus are equal to those registered, therefore requiring further investigation to check if there are any misidentifications of the species after roadkill. After 2018, the number of recorded roadkill of C. elaphus increased (see Table 2). This may be due to the involvement of hunters in the management of roadkill (hunting representatives are informed about roadkill and remove the carcass in the event of an incident in the hunting club area). Misidentification is therefore no longer possible. Some roadkilled male C. elaphus may be illegally taken for trophy purposes if they have developed antlers. However, there is still no clear explanation for the low number of roadkill in this species.
We also find that on average only about 40% of A. alces roadkill and about 20% of C. elaphus and C. capreolus roadkill occurred on roads in forests. Furthermore, this proportion decreased for A. alces and C. capreolus. This finding is significant as it raises questions about the need for further wildlife fencing related to forest habitats.
Unfortunately, fences against wild animals do not always provide the desired effectiveness. One of the most important factors contributing to this fact is the gates in these fences. Unfortunately, these gates are often left open and the fences themselves are not quickly repaired when damaged. In particular, shorter sections of fence are generally ineffective for A. alces. In contrast, newly built fences are more effective. In Lithuania, the current infrastructure does not include viaducts specifically designed to accommodate big game; only underpasses exist, but these are ineffective for A. alces.
Based on the proportion of roadkill compared to the hunted numbers, roadkill were an important factor in A. alces mortality in 2005–2009, but this proportion has diminished to about 10% in recent years. The proportion of roadkilled C. elaphus on average is 1.8%, and therefore it is not important as a source of species mortality compared to the influence of hunting. However, the proportion of roadkilled C. capreolus is still increasing—the values of 16.5%–16.6% in recent years suggest that roadkill of the species is a factor in road safety and an issue in species management.
In terms of road safety, cervid roadkill can pose a serious hazard to motorists, as collisions with these large animals can cause significant damage to vehicles and result in injuries or fatalities. In addition, the presence of large numbers of roadkill can attract scavengers and other animals to the road, increasing the risk of further accidents [45]. From a species management perspective, cervid roadkill can have a negative impact on population numbers and genetic diversity. Large numbers of roadkill can reduce the overall population size and affect the distribution and migration patterns of cervid species. In addition, roadkill can disproportionately affect certain age or sex classes, potentially leading to imbalances in the population [40].
Comparison of roadkill and harvest data in several European countries confirmed that Lithuania has one of the lowest roadkill-harvest proportions in Europe. Based on the 2017 data from [45], the proportion of roadkill of the three cervid species in Lithuania, being 7.0% (CI = 6.9%–7.2%), is similar to that of Latvia at 7.2% (CI = 7.0%–7.4%). It decreases in countries where moose roadkill are minimal, such as Poland (5.8%, CI = 5.7%–5.9%), or are absent, such as in Austria with 3.3% (3.3%–3.4%) or Belgium with 2.2% (CI = 1.9%–2.5%). In Germany, roadkill of C. elaphus and C. capreolus together comprise 15.3% (CI = 15.2%–15.4%) compared to the number of hunted individuals, mainly depending on C. capreolus roadkill (Table 3).
Compared to the hunting bag, the highest influence of roadkill was registered in the Nordic countries: in Norway, roadkill of cervids in 2017 was 20.8% (CI = 20.6%–21.0%) and in Sweden 26.3% (CI = 26.1%–26.5%) compared to hunted individuals, recalculated after Linnell et al. [45]. Both countries were characterized by extremely high C. capreolus roadkill, being nearly half of the respective hunting bag (Table 3).
Roadkill numbers of cervids in Lithuania can be compared to those in Slovenia and Croatia. In Slovenia, between 4000 and 6000 C. capreolus were killed annually in 1999–2001 [42]; this number remained the same in the following decades, the annual average being 5326 individuals in 2010–2019. C. elaphus roadkill in 2010–2019 was less frequent—108 per year on average [73]. However, compared to Lithuania, Slovenia is three times smaller in area. It was noted that roadkill of C. capreolus exceeded 15% of the hunting bag, and this is related to driver safety in the country [42].
In Croatia, 12.0 C. capreolus roadkill, 0.2 C. elaphus roadkill and 5.2 D. dama roadkill per 100 km of state roads were registered annually in 2012–2016 [44]. In Lithuania, the annual averages were 1.38 for A. alces, 0.40 for C. elaphus and 11.64 for C. capreolus individuals per 100 km of main and national roads. Respectively, regional roads contribute 8.7%, 11.7% and 16.3% to these numbers.
It has been proposed to use hunting yield (the same as hunting bag in other publications and here) as the main factor for modeling the distribution of game, including cervids, such as A. alces, C. elaphus, D. dama and C. capreolus [74]. Beyond any doubts, the roadkill of cervids also reflects their distribution and abundances, and therefore roadkill counts are proposed as a means of population monitoring [75] and as a proxy of abundance [76]. However, so far it has not been clear how large the input is of unreported roadkill (and it is therefore not included in official roadkill statistics). Our results show that up to one-third of roadkill of C. capreolus were not reported.
Our results are consistent with other researchers who have highlighted examples of high road mortality in mammals (up to 50%–80% of total mortality in some quolls and opossums, species not indicated) [77]. Roadkill may put at risk not only mammals, but also bird populations in Europe [78] and on a global scale. Four populations of terrestrial mammals could become extinct during the next 50 years due to roadkill. These are the maned wolf (Chrysocyon brachyurus) and little spotted cat (Leopardus tigrinus), both in Brazil, the brown hyena (Hyaena brunnea) in Southern Africa and the leopard (Panthera pardus) in North India [79]. Other threats of roadkill include biases in demography and migration-related effects [77]. In addition to other disturbing human activities such as hunting, roadkill of cervids might be a significant addition to their mortality
Therefore, roadkill are important for both planning ungulate management [18,80] and driver safety measures. In the current situation, where a high abundance of ungulates occurs in many countries [4,5], this might require substantial spending on roadkill mitigation measures. Wildlife fencing is not a panacea, as “wildlife fencing without proper crossing structures re-allocates wildlife movement pathways toward roads with insufficient or no mitigation measures”—p. 1 in [55]. The effectiveness of fences for cervids in Lithuania is insufficient due to their fragmentation, the presence of closable gates in the fences and the lack of overpasses for large game. Still, with the roadkill problem not solved and actually increasing, the economic value of wildlife conservation could be compromised [81]. In addition to the damage of cars, this is because human casualties in UVCs have no measurable financial expressions.

5. Limitations of the Study

In our study, the number of not-reported roadkill is still likely underestimated, as (1) we insufficiently covered regional roads due to their greater length (over 15 thousand kilometers in Lithuania), and (2) some roadkill could be taken off the road, therefore data on it could be completely missing in both sources. However, based on the data of roadkill registered by the Lithuanian Police Traffic Supervision Service, regional roads contribute, depending on the species, only 9%–16% of roadkill, and therefore possible bias from this lack of coverage is not large. Our understanding is that representative studies of roadkill on regional roads can be conducted in cooperation with hunter and road service organizations, or with the staff of protected areas.
Officers of the Lithuanian Police Traffic Supervision Service are not trained on how to recognize roadkilled mammals. In the reported roadkill, the number of “unknown” (unidentified) roadkilled species was high between 2002 and 2018. Therefore, among the reported cases there can be cervids not accounted for. Later on, hunters become involved in roadkill recovery, so the results of roadkill reporting become more reliable.
The length of main and national roads in Lithuania has not changed by more than 5% since 2010 (, accessed on 16 May 2023 compared with [69]), therefore error in not-reported roadkill recalculation is in the same range.

6. Conclusions

Currently, A. alces and C. elaphus roadkill in Lithuania is a moderate source of cervid mortality in comparison to the hunted numbers of these species.
The high number of recorded and the increasing number of unreported roadkill of C. capreolus, the latter being over 9000 from 2002–2022, are both threatening driver safety and should be taken into account in the management planning of this species.
Our study disproves, at least in the Lithuanian context, the notion that deer are mostly roadkilled in forests and that more road fencing through forests is needed to reduce accident rates.

Author Contributions

Conceptualization, L.B. (Linas Balčiauskas); methodology, all authors; investigation, L.B. (Linas Balčiauskas) and L.B. (Laima Balčiauskienė); formal analysis, L.B. (Linas Balčiauskas) and A.K.; data curation, A.K. and L.B. (Laima Balčiauskienė); writing—original draft preparation L.B. (Linas Balčiauskas) and L.B. (Laima Balčiauskienė); writing—review and editing, all authors. All authors have read and agreed to the published version of the manuscript.


In 2019–2022: roadkill surveys were partially funded by the project “Investigations of the Status of Invasive and Alien Species in Lithuania” (Contract No. 05.5.1-APVA-V-018-01-0012).

Data Availability Statement

Due to ongoing investigation, data from this study are available from the corresponding author upon personal request.


We thank Deivis Kulvietis and Artūras Kajėnas, both from the Lithuanian Police Traffic Supervision Service, for their help with roadkill data. We are very grateful to Jos Stratford for his help in data collection and his suggestions for improving the manuscript.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.


  1. Putman, R.J. Ungulates in temperate forest ecosystems: Perspectives and recommendations for future research. Forest Ecol. Manag. 1996, 88, 205–214. [Google Scholar] [CrossRef]
  2. Latham, J. Interspecific interactions of ungulates in European forests: An overview. Forest Ecol. Manag. 1999, 120, 13–21. [Google Scholar] [CrossRef]
  3. Corlatti, L.; Zachos, F.E. (Eds.) Terrestrial Cetartiodactyla; Springer Nature: Cham, Switzerland, 2022; pp. 51–86, 165–195, 215–245. [Google Scholar]
  4. Valente, A.M.; Acevedo, P.; Figueiredo, A.M.; Fonseca, C.; Torres, R.T. Overabundant wild ungulate populations in Europe: Management with consideration of socio-ecological consequences. Mammal Rev. 2020, 50, 353–366. [Google Scholar] [CrossRef]
  5. Carpio, A.J.; Apollonio, M.; Acevedo, P. Wild ungulate overabundance in Europe: Contexts, causes, monitoring and management recommendations. Mammal Rev. 2021, 51, 95–108. [Google Scholar] [CrossRef]
  6. Boulanger, V.; Dupouey, J.-L.; Archaux, F.; Badeau, V.; Baltzinger, C.; Chevalier, R.; Corcket, E.; Dumas, Y.; Forgeard, F.; Mårell, A.; et al. Ungulates increase forest plant species richness to the benefit of non-forest specialists. Glob. Chang. Biol. 2017, 4, e485–e495. [Google Scholar] [CrossRef][Green Version]
  7. Fløjgaard, C.; Bruun, H.H.; Hansen, M.D.; Heilmann-Clausen, J.; Svenning, J.C.; Ejrnæs, R. Are ungulates in forests concerns or key species for conservation and biodiversity? Reply to Boulanger et al. (DOI: 10.1111/gcb. 13899). Glob. Chang. Biol. 2018, 24, 869–871. [Google Scholar] [CrossRef][Green Version]
  8. Kuijper, D.P.; Cromsigt, J.P.; Churski, M.; Adam, B.; Jędrzejewska, B.; Jędrzejewski, W. Do ungulates preferentially feed in forest gaps in European temperate forest? Forest Ecol. Manag. 2009, 258, 1528–1535. [Google Scholar] [CrossRef]
  9. Cirino, D.W.; Lupinetti-Cunha, A.; Freitas, C.H.; de Freitas, S.R. Do the roadkills of different mammal species respond the same way to habitat and matrix? Nat. Conserv. 2022, 47, 65–85. [Google Scholar] [CrossRef]
  10. Kałuziński, J. The occurrence and distribution of field ecotype of roe-deer in Poland. Acta Theriol. 1974, 19, 291–300. [Google Scholar] [CrossRef][Green Version]
  11. Balčiauskas, L.; Wierzchowski, J.; Kučas, A.; Balčiauskienė, L. Habitat suitability based models for ungulate roadkill prognosis. Animals 2020, 10, 1345. [Google Scholar] [CrossRef]
  12. Singleton, P.H.; Lehmkuhl, J.F. Assessing wildlife habitat connectivity in the Interstate 90 Snoqualmie Pass corridor, Washington. In Proceedings of the Third International Conference on Wildlife Ecology and Transportation, Tallahassee, FL, USA, 13–16 September 1999. [Google Scholar]
  13. Colino-Rabanal, V.J.; Peris, S.J. Wildlife road kills: Improving knowledge about ungulate distributions? Hystrix 2016, 27, 91–98. [Google Scholar] [CrossRef]
  14. Dekker, D. Road and rail fatalities of elk, bighorn sheep, and gray wolves in Jasper national park, Alberta, 1980–2018. Northwestern Nat. 2021, 102, 83–88. [Google Scholar] [CrossRef]
  15. Ha, H.; Shilling, F. Modelling potential wildlife-vehicle collisions (WVC) locations using environmental factors and human population density: A case-study from 3 state highways in Central California. Ecol. Inform. 2018, 43, 212–221. [Google Scholar] [CrossRef]
  16. Jakubas, D.; Ryś, M.; Lazarus, M. Factors affecting wildlife-vehicle collision on the expressway in a suburban area in northern Poland. North-West. J. Zool. 2018, 14, e171702. [Google Scholar]
  17. Torres, R.T.; Linck, P.; Pinto, N.; Ares-Pereira, G.; Barroqueiro, C.; Fonseca, C.; Carvalho, J. Landscape and population drivers of ungulate-vehicle collisions in Portugal. Appl. Geogr. 2023, 151, 102859. [Google Scholar] [CrossRef]
  18. Apollonio, M.; Andersen, R.; Putman, R. (Eds.) European Ungulates and Their Management in the 21st Century; Cambridge University Press: New York, NY, USA, 2010; pp. 578–604. [Google Scholar]
  19. Balčiauskas, L.; Kawata, Y.; Balčiauskienė, L. Moose Management Strategies under Changing Legal and Institutional Frameworks. Sustainability 2020, 12, 8482. [Google Scholar] [CrossRef]
  20. Balčiauskas, L.; Kawata, Y. Red Deer in Lithuania: History, Status and Management. Sustainability 2022, 14, 14091. [Google Scholar] [CrossRef]
  21. Kuzyk, G.; Procter, C.; Marshall, S.; Schindler, H.; Schwantje, H.; Scheideman, M.; Hodder, D. Factors affecting Moose population declines in British Columbia. In 2019 Progress Report: February 2012–May 2019; Wildlife Working Report No. WR-127; Ministry of Forests, Lands, Natural Resource Operations and Rural Development: Victoria, BC, Canada, 2019; p. 73. [Google Scholar]
  22. Bruinderink, G.G.; Hazebroek, E. Ungulate traffic collisions in Europe. Conserv. Biol. 1996, 10, 1059–1067. [Google Scholar] [CrossRef]
  23. Mysterud, A. Temporal variation in the number of car-killed red deer Cervus elaphus in Norway. Wildl. Biol. 2004, 10, 203–211. [Google Scholar] [CrossRef]
  24. Seiler, A. Trends and spatial patterns in ungulate-vehicle collisions in Sweden. Wildl. Biol. 2004, 10, 301–313. [Google Scholar] [CrossRef]
  25. Rolandsen, C.M.; Solberg, E.; Herfindal, I.; Moorter, B.V.; Sæther, B.-E. Large-scale spatiotemporal variation in road mortality of moose: Is it all about population density? Ecosphere 2011, 2, 113. [Google Scholar] [CrossRef]
  26. Niemi, M.; Rolandsen, C.M.; Neumann, W.; Kukko, T.; Tiilikainen, R.; Pusenius, J.; Solberg, E.J.; Ericsson, G. Temporal patterns of moose-vehicle collisions with and without personal injuries. Accid. Anal. Prev. 2017, 98, 167–173. [Google Scholar] [CrossRef] [PubMed][Green Version]
  27. Nelli, L.; Langbein, J.; Watson, P.; Putman, R. Mapping risk: Quantifying and predicting the risk of deer-vehicle collisions on major roads in England. Mamm. Biol. 2018, 91, 71–78. [Google Scholar] [CrossRef]
  28. Raymond, S.; Schwartz, A.L.; Thomas, R.J.; Chadwick, E.; Perkins, S.E. Temporal patterns of wildlife roadkill in the UK. PLoS ONE 2021, 16, e0258083. [Google Scholar] [CrossRef]
  29. Shilling, F.; Perkins, S.E.; Collinson, W. Wildlife/roadkill observation and reporting systems. In Handbook of Road Ecology; van der Ree, R., Smith, D.J., Grilo, C., Eds.; John Wiley & Sons: Chichester, UK, 2015; pp. 492–501. [Google Scholar]
  30. Wright, P.G.; Coomber, F.G.; Bellamy, C.C.; Perkins, S.E.; Mathews, F. Predicting hedgehog mortality risks on British roads using habitat suitability modelling. PeerJ 2020, 7, e8154. [Google Scholar] [CrossRef][Green Version]
  31. Kent, E.; Schwartz, A.L.; Perkins, S.E. Life in the fast lane: Roadkill risk along an urban–rural gradient. J. Urban Ecol. 2021, 7, juaa039. [Google Scholar] [CrossRef]
  32. Morelle, K.; Lejeune, P. Seasonal variations of wild boar Sus scrofa distribution in agricultural landscapes: A species distribution modelling approach. Eur. J. Wildl. Res. 2015, 61, 45–56. [Google Scholar] [CrossRef]
  33. Morelle, K.; Lehaire, F.; Lejeune, P. Spatio-temporal patterns of wildlife-vehicle collisions in a region with a high-density road network. Nat. Conserv. 2013, 5, 53–73. [Google Scholar] [CrossRef][Green Version]
  34. Sèbe, M.; Briton, F.; Kinds, A. Does predation by wolves reduce collisions between ungulates and vehicles in France? Hum. Dimens. Wildl. 2022, 28, 281–293. [Google Scholar] [CrossRef]
  35. Lagos, L.; Picos, J.; Valero, E. Temporal pattern of wild ungulate-related traffic accidents in northwest Spain. Eur. J. Wildl. Res. 2012, 58, 661–668. [Google Scholar] [CrossRef]
  36. Rodríguez-Morales, B.; Díaz-Varela, E.R.; Marey-Pérez, M.F. Spatiotemporal analysis of vehicle collisions involving wild boar and roe deer in NW Spain. Accid. Anal. Prev. 2013, 60, 121–133. [Google Scholar] [CrossRef] [PubMed]
  37. Valero, E.; Picos, J.; Lagos, L.; Álvarez, X. Road and traffic factors correlated to wildlife–vehicle collisions in Galicia (Spain). Wildl. Res. 2015, 42, 25–34. [Google Scholar] [CrossRef]
  38. Sáenz-de-Santa-María, A.; Tellería, J.L. Wildlife-vehicle collisions in Spain. Eur. J. Wildlife Res. 2015, 61, 399–406. [Google Scholar] [CrossRef]
  39. Bíl, M.; Kubeček, J.; Sedoník, J.; Andrášik, R. Srazenazver. cz: A system for evidence of animal-vehicle collisions along transportation networks. Biol. Conserv. 2017, 213, 167–174. [Google Scholar] [CrossRef]
  40. Mrtka, J.; Borkovcová, M. Estimated mortality of mammals and the costs associated with animal–vehicle collisions on the roads in the Czech Republic. Transport. Res. D-Tr. E. 2013, 18, 51–54. [Google Scholar] [CrossRef]
  41. Bartonička, T.; Andrášik, R.; Duľa, M.; Sedoník, J.; Bíl, M. Identification of local factors causing clustering of animal-vehicle collisions. J. Wildl. Manag. 2018, 82, 940–947. [Google Scholar] [CrossRef][Green Version]
  42. Bíl, M.; Kubeček, J.; Andrášik, R. Ungulate-vehicle collision risk and traffic volume on roads. Eur. J. Wildl. Res. 2020, 66, 59. [Google Scholar] [CrossRef]
  43. Pokorny, B. Roe deer-vehicle collisions in Slovenia: Situation, mitigation strategy and countermeasures. Vet. Arhiv 2006, 76, 177–187. [Google Scholar]
  44. Vrkljan, J.; Hozjan, D.; Barić, D.; Ugarković, D.; Krapinec, K. Temporal patterns of vehicle collisions with roe deer and wild boar in the dinaric area. Croat. J. For. Eng. J. Theory Appl. For. Eng. 2020, 41, 1–13. [Google Scholar] [CrossRef]
  45. Linnell, J.D.; Cretois, B.; Nilsen, E.B.; Rolandsen, C.M.; Solberg, E.J.; Veiberg, V.; Kaczensky, P.; Van Moorter, B.; Panzacchi, M.; Rauset, G.; et al. The challenges and opportunities of coexisting with wild ungulates in the human-dominated landscapes of Europe's Anthropocene. Biol. Conserv. 2020, 244, 108500. [Google Scholar] [CrossRef]
  46. Kruuse, M.; Enno, S.E.; Oja, T. Temporal patterns of wild boar-vehicle collisions in Estonia, at the northern limit of its range. Eur. J. Wildl. Res. 2016, 62, 787–791. [Google Scholar] [CrossRef]
  47. Balčiauskas, L.; Balčiauskienė, L. Wildlife-vehicle accidents in Lithuania, 2002–2007. Acta Biol. Univ. Daugavp. 2008, 8, 89–94. [Google Scholar]
  48. Balčiauskas, L. Distribution of species-specific wildlife–vehicle accidents on Lithuanian roads, 2002–2007. Est. J. Ecol. 2009, 58, 157–168. [Google Scholar] [CrossRef]
  49. Balčiauskas, L. The Influence of Roadkill on Protected Species and Other Wildlife in Lithuania. In Proceedings of the ICOET 2011 Proceedings, Seatle, WA, USA, 21–25 August 2011. [Google Scholar]
  50. Balčiauskas, L.; Jasiulionis, M. Reducing the incidence of mammals on public highways using chemical repellent. Balt. J. Road Bridge Eng. 2012, 7, 92–97. [Google Scholar] [CrossRef]
  51. Wierzchowski, J.; Kučas, A.; Balčiauskas, L. Application of least-cost movement modeling in planning wildlife mitigation measures along transport corridors: Case study of forests and moose in Lithuania. Forests 2019, 10, 831. [Google Scholar] [CrossRef][Green Version]
  52. Kučas, A.; Balčiauskas, L. Temporal patterns of ungulate-vehicle collisions in Lithuania. J. Environ. Manag. 2020, 273, 111172. [Google Scholar] [CrossRef]
  53. Balčiauskas, L.; Stratford, J.; Balčiauskienė, L.; Kučas, A. Importance of professional roadkill data in assessing diversity of mammal roadkills. Transp. Res. D Transp. Environ. 2020, 87, 102493. [Google Scholar] [CrossRef]
  54. Kučas, A.; Balčiauskas, L. Roadkill-data-based identification and ranking of mammal habitats. Land 2021, 10, 477. [Google Scholar] [CrossRef]
  55. Kučas, A.; Balčiauskas, L. Impact of road fencing on ungulate-vehicle collisions and hotspot patterns. Land 2021, 10, 338. [Google Scholar] [CrossRef]
  56. Orlowski, G.; Nowak, L. Factors influencing mammal roadkills in the agricultural landscape of south-western Poland. Pol. J. Ecol. 2006, 54, 283–294. [Google Scholar]
  57. Smits, R.; Bohatkiewicz, J.; Bohatkiewicz, J.; Hałucha, M. A Geospatial Multi-scale Level Analysis of the Distribution of Animal-Vehicle Collisions on Polish Highways and National Roads. In Proceedings of the International Conference “Vision Zero for Sustainable Road Safety in Baltic Sea Region”, Vilnius, Lithuania, 5–6 December 2018. [Google Scholar]
  58. Krukowicz, T.; Firląg, K.; Chrobot, P. Spatiotemporal analysis of road crashes with animals in Poland. Sustainability 2022, 14, 1253. [Google Scholar] [CrossRef]
  59. Basak, S.M.; O’Mahony, D.T.; Lesiak, M.; Basak, A.K.; Ziółkowska, E.; Kaim, D.; Hossain, M.S.; Wierzbowska, I.A. Animal-vehicle collisions during the COVID-19 lockdown in early 2020 in the Krakow metropolitan region, Poland. Sci. Rep. 2022, 12, 7572. [Google Scholar] [CrossRef]
  60. Borowik, T.; Ratkiewicz, M.; Maślanko, W.; Kowalczyk, R.; Duda, N.; Żmihorski, M. Temporal pattern of moose-vehicle collisions. Transp. Res. D Transp. Environ. 2021, 92, 1027. [Google Scholar] [CrossRef]
  61. Tajchman, K.; Gawryluk, A.; Drozd, L.; Czyżowski, P.; Karpiński, M.; Goleman, M. Deer-vehicle collisions in Lubelskie region in Poland. Safety coefficients. Appl. Ecol. Environ. Res. 2017, 15, 1485–1498. [Google Scholar] [CrossRef]
  62. Andersen, D.; Jang, Y. Biodiversity and Transportation Infrastructure in the Republic of Korea: A Review on Impacts and Mitigation in Developing the Country. Diversity 2021, 13, 519. [Google Scholar] [CrossRef]
  63. Kang, W.; Minor, E.S.; Woo, D.; Lee, D.; Park, C.R. Forest mammal roadkills as related to habitat connectivity in protected areas. Biodivers. Conserv. 2016, 25, 2673–2686. [Google Scholar] [CrossRef]
  64. Waetjen, D.P.; Shilling, F.M. Large extent volunteer roadkil16l and wildlife observation systems as sources of reliable data. Front. Ecol. Evol. 2017, 5, 89. [Google Scholar] [CrossRef][Green Version]
  65. Tiedeman, K.; Hijmans, R.J.; Mandel, A.; Waetjen, D.P.; Shilling, F. The quality and contribution of volunteer collected animal vehicle collision data in ecological research. Ecol. Indic. 2019, 106, 105431. [Google Scholar] [CrossRef]
  66. Bíl, M.; Andrášik, R. The effect of wildlife carcass underreporting on KDE+ hotspots identification and importance. J. Environ. Manag. 2020, 275, 111254. [Google Scholar] [CrossRef] [PubMed]
  67. European Environmental Agency. Lithuania Land cover Country Fact Sheet. 2012. Available online: (accessed on 1 March 2023).
  68. Balčiauskas, L.; Trakimas, G.; Juškaitis, R.; Ulevičius, A.; Balčiauskienė, L. Lietuvos žinduolių, varliagyvių ir roplių atlasas. In Atlas of Lithuanian Mammals, Amphibians and Reptiles, 2nd ed.; Akstis: Vilnius, Lithuania, 1999; p. 112. [Google Scholar]
  69. Ministry of Transport and Communications. Roads and Road Transport. Available online: (accessed on 12 March 2023).
  70. Valstybinės Reikšmės Kelių Duomenys. Available online: (accessed on 16 April 2023).
  71. G-Test Calculator. Available online: (accessed on 10 January 2023).
  72. How to Calculate CAGR (Compound Annual Growth Rate) in Excel. Available online: (accessed on 22 March 2023).
  73. Pokorny, B.; Cerri, J.; Bužan, E. Wildlife roadkill and COVID-19: A biologically significant, but heterogeneous, reduction. J. Appl. Ecol. 2022, 59, 1291–1301. [Google Scholar] [CrossRef]
  74. ENETWILD-consortium; Illanas, S.; Croft, S.; Smith, G.C.; López-Padilla, S.; Vicente, J.; Blanco-Aguiar, J.A.; Scandura, M.; Apollonio, M.; Ferroglio, E.; et al. New models for wild ungulates occurrence and hunting yield abundance at European scale. EFSA Support. Publ. 2022, 19, 7631E. [Google Scholar] [CrossRef]
  75. Canova, L.; Balestrieri, A. Long-term monitoring by roadkill counts of mammal populations living in intensively cultivated landscapes. Biodivers. Conserv. 2019, 28, 97–113. [Google Scholar] [CrossRef]
  76. Fernández-López, J.; Blanco-Aguiar, J.A.; Vicente, J.; Acevedo, P. Can we model distribution of population abundance from wildlife–vehicles collision data? Ecography 2022, 5, e06113. [Google Scholar] [CrossRef]
  77. Moore, L.J.; Petrovan, S.O.; Bates, A.J.; Hicks, H.L.; Baker, P.J.; Perkins, S.E.; Yarnell, R.W. Demographic effects of road mortality on mammalian populations: A systematic review. Biol. Rev. 2023. [Google Scholar] [CrossRef]
  78. Grilo, C.; Koroleva, E.; Andrášik, R.; Bíl, M.; González-Suárez, M. Roadkill risk and population vulnerability in European birds and mammals. Front. Ecol. Environ. 2020, 18, 323–328. [Google Scholar] [CrossRef]
  79. Grilo, C.; Borda-de-Água, L.; Beja, P.; Goolsby, E.; Soanes, K.; le Roux, A.; Koroleva, E.; Ferreira, F.Z.; Gagné, S.A.; Wang, Y.; et al. Conservation threats from roadkill in the global road network. Global Ecol. Biogeogr. 2021, 30, 2200–2210. [Google Scholar] [CrossRef]
  80. Apollonio, M.; Belkin, V.V.; Borkowski, J.; Borodin, O.I.; Borowik, T.; Cagnacci, F.; Danilkin, A.A.; Danilov, P.I.; Faybich, A.; Ferretti, F.; et al. Challenges and science-based implications for modern management and conservation of European ungulate populations. Mammal Res. 2017, 62, 209–217. [Google Scholar] [CrossRef][Green Version]
  81. Martino, S.; Kenter, J.O. Economic valuation of wildlife conservation. Eur. J. Wildl. Res. 2023, 69, 32. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Study area with forests and roads by categories; labels show the unique identification numbers of the main roads/highways in Lithuania.
Figure 1. Study area with forests and roads by categories; labels show the unique identification numbers of the main roads/highways in Lithuania.
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Figure 2. Cumulative annual cervid roadkill based on registered data (blue) and projected estimates from the unreported data (orange) between 2002–2022: (a) Alces alces, (b) Cervus elaphus, (c) Capreolus capreolus.
Figure 2. Cumulative annual cervid roadkill based on registered data (blue) and projected estimates from the unreported data (orange) between 2002–2022: (a) Alces alces, (b) Cervus elaphus, (c) Capreolus capreolus.
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Figure 3. Occurrence of Alces alces roadkill in respect to forest habitat, 2002–2022. One dot represents one roadkill.
Figure 3. Occurrence of Alces alces roadkill in respect to forest habitat, 2002–2022. One dot represents one roadkill.
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Figure 4. Occurrence of Cervus elaphus roadkill in respect to forest habitat, 2002–2022. One dot represents one roadkill.
Figure 4. Occurrence of Cervus elaphus roadkill in respect to forest habitat, 2002–2022. One dot represents one roadkill.
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Figure 5. Occurrence of Capreolus capreolus roadkill in respect to forest habitat, 2002–2022. One dot represents one roadkill.
Figure 5. Occurrence of Capreolus capreolus roadkill in respect to forest habitat, 2002–2022. One dot represents one roadkill.
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Figure 6. Changes of the hunting bag (in % from the surveyed numbers) and proportions of the roadkilled cervid numbers (in % from the hunting bag size) in Lithuania, 2002–2021. A one-year ban was in place in 2004 on the hunting of A. alces and C. elaphus. Significant trends (p < 0.005) indicated as solid red lines on each chart, insignificant trends shown as dotted lines.
Figure 6. Changes of the hunting bag (in % from the surveyed numbers) and proportions of the roadkilled cervid numbers (in % from the hunting bag size) in Lithuania, 2002–2021. A one-year ban was in place in 2004 on the hunting of A. alces and C. elaphus. Significant trends (p < 0.005) indicated as solid red lines on each chart, insignificant trends shown as dotted lines.
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Table 1. Route lengths (main plus national roads, in km) and cervid roadkill indexes (RI ± SE, rounded to five decimal places) used to estimate the number of cervids that were killed on main and national roads by vehicles in Lithuania between 2002–2022 but not reported. Source: data of professional roadkill registration.
Table 1. Route lengths (main plus national roads, in km) and cervid roadkill indexes (RI ± SE, rounded to five decimal places) used to estimate the number of cervids that were killed on main and national roads by vehicles in Lithuania between 2002–2022 but not reported. Source: data of professional roadkill registration.
Year Route LengthAlces alcesCervus elaphusCapreolus capreolus
20052767.0000.00022 ± 0.00022
20078423.2000.00015 ± 0.00015
200970,205.20.00002 ± 0.000020.00003 ± 0.000020.00011 ± 0.00006
20123916.0000.00016 ± 0.00016
201318,079.000.00005 ± 0.000050.00006 ± 0.00005
201436,175.2000.00051 ± 0.00020
201518,272.3000.00018 ± 0.00011
201612,275.80.00013 ± 0.000130.00003 ± 0.000030.00126 ± 0.00052
201710,653.400.00015 ± 0.000150.00027 ± 0.00014
20188625.9000.00003 ± 0.00003
20197029.3000.00028 ± 0.00016
202029,382.500.00001 ± 0.000010.00022 ± 0.00010
202121,073.20 0.00019 ± 0.00011
202218,737.200.00003 ± 0.000030.00013 ± 0.00007
Table 2. Cervid roadkill related to the forest habitat in 2002–2022. The proportion of the forest roadkill was calculated from the total numbers.
Table 2. Cervid roadkill related to the forest habitat in 2002–2022. The proportion of the forest roadkill was calculated from the total numbers.
YearNon-Forest Roadkill, nForest Roadkill, nForest Roadkill, %
A. alcesC. elaphusC. capreolusA. alcesC. elaphusC. capreolusA. alcesC. elaphusC. capreolus
Table 3. Harvested and roadkilled numbers of cervids in European countries in 2017; percentages show the proportion of roadkill compared to the hunted numbers.
Table 3. Harvested and roadkilled numbers of cervids in European countries in 2017; percentages show the proportion of roadkill compared to the hunted numbers.
CountryHunting Bag 1Roadkill 1%
A. alcesC. elaphusC. capreolusA. alcesC. elaphusC. capreolusA. alcesC. elaphusC. capreolus
Germany 76,7941,190,724 2920191,591 3.816.1
Austria 2 61,545285,718 66310,897 1.13.8
Belgium 2 55762 127 2.2
1—data from [45]; comparable data are not available for other countries. 2A. alces not present; populations of C. elaphus and C. capreolus are contiguous.
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Balčiauskas, L.; Kučas, A.; Balčiauskienė, L. The Impact of Roadkill on Cervid Populations in Lithuania. Forests 2023, 14, 1224.

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Balčiauskas L, Kučas A, Balčiauskienė L. The Impact of Roadkill on Cervid Populations in Lithuania. Forests. 2023; 14(6):1224.

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Balčiauskas, Linas, Andrius Kučas, and Laima Balčiauskienė. 2023. "The Impact of Roadkill on Cervid Populations in Lithuania" Forests 14, no. 6: 1224.

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