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Article

Factors Affecting Roadkills of Semi-Aquatic Mammals

by
Linas Balčiauskas
*,
Jos Stratford
,
Andrius Kučas
† and
Laima Balčiauskienė
Nature Research Centre, Akademijos 2, 08412 Vilnius, Lithuania
*
Author to whom correspondence should be addressed.
†
Current Affiliation: Joint Research Centre, the European Commission, Via Fermi 2749, I-21027 Ispra, Italy.
Biology 2022, 11(5), 748; https://doi.org/10.3390/biology11050748
Submission received: 4 April 2022 / Revised: 24 April 2022 / Accepted: 11 May 2022 / Published: 13 May 2022
(This article belongs to the Special Issue Biodiversity Patterns)
Browse Figures
Review Reports Versions Notes

Abstract

:

Simple Summary

Semiaquatic mammal roadkill, specifically the European beaver, Eurasian otter, American mink and muskrat was analyzed in Lithuania in the period 2002–2021. From almost 40,000 recorded roadkill cases for all species, these species were registered 60, 22, 26 and 3 times, respectively, thus being rare events. Registered beaver roadkill numbers correlated with an increased registration effort and traffic intensity, while otter roadkill correlated with a registration effort only, and mink roadkill correlated with an increased population (estimated through its proxy and the number of hunted individuals per year). The roadkill was not always in close proximity to waterbodies, with 38–54% of roadkill of these species occurring over 200 m from the nearest water source. With the American mink and muskrat being invasive species in the EU and the Eurasian otter protected in many countries, it could be considered valuable to enhance the registrations of their roadkill (using targeted efforts by drivers, hunters or other citizen scientists) in order to obtain the extrapolated amount of roadkill and to use this knowledge in species management.

Abstract

We previously showed that registration efforts and traffic intensity explain 90% of variation in mammal roadkill numbers, 70% of variation in the numbers of recorded species and 40% of diversity variation. Here we analyze semiaquatic mammal roadkill in Lithuania in 2002–2021, relating these to the monitoring effort. From 39,936 analyzed roadkill, the European beaver (Castor fiber) was registered 60 times, American mink (Neovison vison) 26 times, otter (Lutra lutra) 22 times and muskrat (Ondatra zibethica) 3 times. The average roadkill indexes were 0.000065, 0.00076, 0.00061 and 0.00010 ind./km/day, and the extrapolated annual roadkill for the country was 44–357, 36–456, 49–464 and 89–144 individuals, respectively. Beaver roadkill numbers correlated with the registration effort and traffic intensity, otter roadkill with registration effort only and mink with hunting bag (number of hunted individuals per year). Roadkill was not always related to proximity to water, with 38–54% of roadkill occurring over 200 m from the nearest water source. With American mink and muskrat being invasive species in the EU and otter protected in many countries, it is valuable to enhance the registrations of their roadkill (using targeted efforts by drivers, hunters or other citizen scientists) to obtain the extrapolated amount of roadkill and to use this knowledge in species management.

Graphical Abstract

1. Introduction

Semiaquatic mammals form an ecological group of animals whose lifestyles are related both to aquatic and terrestrial habitats with varying degrees of dependence [1,2]. Four such medium-sized species are present in Lithuania, namely European beaver (Castor fiber), Eurasian otter (Lutra lutra), American mink (Neovison vison) and muskrat (Ondatra zibethicus) [3], the list of species being similar to that in North America [4]. Of these species, C. fiber is considered an ecosystem engineer as it modifies ecosystems and enhances habitat suitability for the other animals [5,6].
The habitats used by this group of mammals include various waterbodies, such as swamps, streams, rivers and lakes [7] with seasonal water level changes having a significant influence [4]. Urban and peri-urban landscapes are also being inhabited [8]. In Lithuania, C. fiber is widespread and inhabits nearly all waterbodies across the country, especially those with shores overgrown with deciduous trees and shrubs. It is abundant in land reclamation channels and builds dams to raise water levels where required. Even small pools and swamps in agricultural landscapes are inhabited, at least temporarily, while there are food sources available [9]. O. zibethicus inhabits waterbodies abounding in water vegetation, such as swamps, lakes and slow river ox-bows. L. lutra is also widespread and inhabits, at least temporarily, all types of waterbodies with running water, including reclamation channels, as well as lakes and ponds. N. vison is found in river valleys and inhabits ponds and swampy areas [10,11]. The latter two species are characterized by high habitat plasticity and by their frequent use of beaver-made dams, lodges and burrows [12]. Habitat use by all of these species is not stable but changes over the medium-term period, e.g., over decades in relation to population changes [13].
Among these semiaquatic mammals, C. fiber is a game species with an unlimited quota of hunted animals per year, hereafter referred to as “hunting bag”. Hunting totals amounted to 19,997, 19,907 and 15,561 individuals in 2019, 2020 and 2021. N. vison and O. zibethicus are invasive species in Lithuania, and there are no limits on hunting, though the hunting bag is quite small. In 2019–2021, the annual hunting bag of N. vison was 42–53, while that of O. zibethicus decreased from 105 ind. in 2019 to 23 ind. in 2021. L. lutra is no longer listed in the Red Data Book of Lithuania [14], but it is not hunted.
In Lithuania and neighboring Poland, semiaquatic mammals are not frequently involved in wildlife–vehicle collisions (WVCs) [15,16]. Despite different species compositions, this is also true in countries with quite different climates [17,18,19,20,21]. A notable exception, however, is coypu (Myocastor coypus), the most frequent (67.6% of casualties) roadkill mammal in the central River Po plain in Italy [22].
Despite the scarcity of semiaquatic mammals in WVCs, they are, however, the highest single cause of mortality for L. lutra in most European countries [23]. The amount of general roadkill has shown a tendency to increase in recent years, both in Lithuania [24] and in other European countries [15,25,26,27] and elsewhere ([28,29,30], etc.).
Several groups of factors are involved in WVCs, these concentrating in some locations to form WVC hotspots [15,30]. The first group of factors relates to transport and driver variables, these including the annual average daily traffic volume (AADT) and speed [16,31], vehicle types [16,32] and driver behavior [33]. The second group of factors are those pertaining to the mammal species themselves, most particularly the population densities of the roadkill species [34]. The two above-mentioned factors, specifically traffic intensity and population size, explained 63–93% of the variation in roadkill numbers in ungulates in Lithuania [16]. The third group of factors includes landscape and terrain factors, such as habitat composition and distances to certain habitats ([35,36,37,38] and references therein). The fourth group includes time factors, including season and time of day [25,39,40], and weather factors [29].
Not all investigations have confirmed the relationship between roadkill frequencies and population sizes, for example [15]. This cited investigation, however, was based on a small-scale analysis and used population numbers only in the nearest hunting districts. Similarly, a low correlation was also found on a local scale in Lithuania between raccoon dog (Nyctereutes procyonoides) numbers and roadkill [38]. Moreover, habitat preferences in semiaquatic mammals may have some specificity: the best survival might be observed not in the most frequently selected habitats. In addition, changes in territory use in these species might be rapid and significant [13].
The aim of this paper is to present long-term data on semiaquatic mammal roadkill in Lithuania, including extrapolating to estimate national roadkill numbers. For this study, we looked at the four affected species, namely C. fiber, L. lutra, N. vison and O. zibethicus. We tested (i) if semiaquatic mammal roadkill was related to proximity to waterbodies (rivers and lakes) or, in the cases of C. fiber and O. zibethicus, to wetlands and (ii) if there is any relationship among roadkill frequencies and population/hunting bag sizes, transport intensity and sampling effort.

2. Material and Methods

We used data collected by professionals at the Nature Research Centre. A registration session was defined as registering roadkill along any length of a same-numbered road travelled on the same day. A total of 3570 such sessions were conducted nationally from 2007–2021, with the total length of all driven routes equaling 275,300 km. The most frequent registration sessions were performed on the main roads A1, A2, A14 and A8 (405, 405, 400 and 210 sessions, respectively). The number of sessions on other main, national and regional roads (Figure 1) varied from 1–125. Data collected by the Lithuanian Police Traffic Supervision Service between 2002 and 2021 were also used. In total, we analyzed data on 39,936 WVCs involving wild mammals (MVCs). In both datasets, individuals not identifying with the species level (including “mustelid”, “small carnivore”, “hare”, “big game”, etc.) were pooled under category “unknown”.
Domestic mammal roadkill (n = 3150) was not analyzed in detail, as these species are mostly related to settlements not habitats. The most frequent roadkill in this category was dogs (n = 2134), cattle (n = 414), horses (n = 220) and cats (n = 220), followed by sheep, goats, pigs and rabbits in descending numbers.
In 2021, there were 21,237 km of state roads of national significance, these being 1751 km of main roads (AADT 3000–20,000), 4928 km of national roads (AADT 500–3000) and 14,559 km of regional roads (AADT up to 500) [41,42]. Changes in road lengths since 2017 [43] have been negligible and, thus, were ignored.
Roadkill indices were calculated as the number of killed animals per km/day. “Empty routes”, where roadkill had already possibly been removed, were included into the yearly index. Estimations of the number of animals killed at the country level for each of the four species were based on the length of the season of actual registrations (Figure 2). L. lutra and N. vison were not registered in January and February; therefore, the season length was set at 300 days, while C. fiber was not registered in February, and thus the season length was set at 330 days. Based on the ecology of species [44] and the registered roadkill, the season length for O. zibethicus was set at 240 days. Calculations were done using the total lengths of the main and national roads only (6679 km) as the roadkill registration efforts on the regional roads were not sufficient. The presented estimations can, therefore, be seen as minimum numbers.
Data on population size and hunted numbers of C. fiber and hunted numbers of N. vison and O. zibethicus are presented in Table 1. This information was collected over a longer time period, but earlier information is not available as open data anymore [45]. N. vison and O. zibethicus are not included in game survey programs; therefore, only information on the bag size was available. L. lutra is a protected species; therefore, there is no hunting bag, and the species is not included in Table 1. As a result, the numbers of L. lutra and N. vison that we used are based on monitoring data and the distribution of these species along rivers [12,46,47,48].
In summary, the population of C. fiber in the years since 2010 has ranged from 40 to 50 thousand individuals (Table 1), while that of L. lutra has been 3000–5000 individuals and that of N. vison in the region of 5000–10,000 individuals [48,49]. The numbers of O. zibethicus have sharply declined since the 1990s and based on the reduced hunting bag, could be 2000–3000 in the last decade [48].
Data on annual average daily traffic (AADT) for the roads with semiaquatic mammal roadkill were obtained from the Lithuanian Road Administration under the Ministry of Transport and Communications [42].
Distances from the roadkill to the nearest waterbody were investigated at the (1) medium spatial scale using the Corine land cover inventory (CLC) 1:50,000 [50] and (2) fine scale using the spatial dataset of (geo) reference base cadastre (GRPK) 1:10,000 (https://www.geoportal.lt/metadata-catalog/catalog/search/resource/details.page?uuid=%7B513C0C29-0447-CB3D-4585-2390144D20D2%7D accessed on 10 February 2022). In the CLC, two land cover classes (411 inland marshes and 412 peat bogs) were used as “wetlands” and two classes of waterbodies (511 water courses and 512 waterbodies) as “water”. Due to the absence of roadkill in the relevant areas, two classes (521 coastal lagoons and 523 sea and ocean) were not analyzed. In the GPRK, we used two habitats, namely “rivers” and “lakes”. Distances from the roadkill points to the closest polygon were measured using near distance measurement tools (https://pro.arcgis.com/en/pro-app/2.8/tool-reference/analysis/near.htm accessed on 5 February 2022) that are part of the standard ArcGIS Proximity toolset (https://pro.arcgis.com/en/pro-app/2.8/tool-reference/analysis/an-overview-of-the-proximity-toolset.htm accessed on 5 February 2022). As distance statistics, species-based averages and SE were calculated. After finding that the distribution of distances did not conform to normal, between-species differences were evaluated with the nonparametric Kruskal–Wallis H test.
At the country scale, we applied a multiple regression analysis to the annual C. fiber and N. vison roadkill to test the possible cumulative influence of the categorical predictors, specifically annual population and hunting bag sizes, sampling effort and AADT. The confidence level was set as p < 0.05. At the local scale, we tested if there was a correlation among roadkill, sampling effort and AADT using the road as the sampling unit. The Pearson correlation coefficient was used. Calculations were done in Statistica for Windows, ver. 6.0 (StatSoft, Inc., Tulsa, OK, USA).
The proportion of semiaquatic mammal roadkill and the 95% CI for species proportion among all wild mammal roadkill were calculated with the Wilson method of the score interval [51] using OpenEpi epidemiological software [52]. Differences in the proportions of roadkill were evaluated using the G test online calculator [53].

3. Results

3.1. Species Composition of the Wild Mammal Roadkills

In the analyzed roadkill, 32 wild mammal species were registered in 2002–2021 (Table 2). Unidentified species accounted for 8.30%, with the pooled number for roadkill being < 20 per species – 0.12% (Table 2). Semiaquatic mammals accounted for only a marginal proportion of the total amount of roadkill registered, this being 0.15% of all roadkill (CI = 0.12–0.19%) for C. fiber, 0.065% (CI = 0.044–0.095%) for N. vison, 0.055% (CI = 0.036–0.083%) for L. lutra and 0.0075% for O. zibethicus. Based on the G-test (G = 62.43), the proportion of C. fiber roadkills was higher (p < 0.001) and that of O. zibethicus lower (p < 0.003) than the other two species.

3.2. Spatiotemporal Distribution and Index of Semiaquatic Mammal Roadkill

The spatial distributions of the semiaquatic roadkill species show C. fiber registrations to be scattered across the county (Figure 3a), while L. lutra roadkill was largely absent in the north of the country (Figure 3b). Most N. vison roadkill was concentrated in the eastern part of the country (Figure 3c), especially on the main road A14 (Figure 1), this characterized by the highest sampling effort.
There were no differences in the annual proportions of the N. vison (G = 19.5, NS), O. zibethicus (G = 2.3, NS) or L. lutra (G = 11.0, NS). C. fiber roadkills has increased in number in the last decade (Figure 4), and annual differences are significant (G = 72.3, p < 0.0001), the most notable increase being in 2021 (G = 32.8, p < 0.0001).
Based on the long-term observations, the average roadkill indexes of the semiaquatic mammals were 0.000062 ± 0.000023 ind./km/day for C. fiber, 0.000077 ± 0.000023 ind./km/day for L. lutra, 0.000074 ± 0.000015 ind./km/day for N. vison and 0.000010 ± 0.000006 ind./km/day for O. zibethicus. On average, the amount of roadkill in these species per year was 124 (95% CI = 33–214), 154 (65–243), 149 (85–213) and 16 (0–34) individuals, respectively. The dynamics of the extrapolated roadkill numbers for the country is shown in Figure 5. The annual roadkill of C. fiber was between 44 ind. in 2020 and 357 ind. in 2017, that of L. lutra between 36 ind. in 2013 and 456 ind. in 2021 and that of N. vison between 49 in 2021 and 464 in 2015. The predicted roadkill of O. zibethicus was 89–144 individuals.
Due to inaccuracies arising from the limited amount of survey, we exclude the predicted amount of L. lutra roadkill in 2004. The figure calculated was in the order of 6000 individuals per year, a strikingly high figure.

3.3. Factors Affecting Semi-Aquatic Mammal Roadkills

At the local scale, using the road as the sampling unit, we analyzed correlations between semiaquatic animal roadkill numbers and the sampling effort (Figure 6) and traffic intensity (Figure 7). The enhanced registration effort yielded higher numbers of registered roadkill of C. fiber and L. lutra (r = 0.9, p < 0.001 and r = 0.69, p < 0.02), but not N. vison (r = 0.53, NS). Most roadkill was registered on a relatively short segment of the A14 main road (Figure 3) in the period of 2007–2021. This was sampled on a weekly basis with exception of the years 2019 and 2020. All three O. zibethicus registrations were on roads with high a sampling effort.
Traffic intensity was correlated with C. fiber roadkill numbers (r = 0.68, p < 0.001), but did not correlate with L. lutra or N. vison roadkill numbers (r = 0.34 and r = −0.07, both NS; Figure 7). However, the semiaquatic mammals did tend to be killed on the main roads that were characterized by the highest AADT and traffic speed. Such roads accounted for 100% of N. vison and O. zibethicus roadkill, 77.3% of L. lutra and 46.7% of C. fiber. National roads, with medium AADT, accounted for 35.0% of C. fiber roadkill and 18.2% of L. lutra roadkill, while on regional roads with low AADT, the respective values were just 10.0% and 4.5%.
At the country scale, we used regression analysis to see if the amount of annually registered semiaquatic animal roadkill was related to the annual averages of AADT, the sum of sampling effort, the hunting bag size of C. fiber and N. vison and the population size of C. fiber. The model for C. fiber was not significant (F3,14 = 1.97, p = 0.16) and explained less than 20% of roadkill variation (R2 = 0.19. Of the four analyzed factors for this species, only AADT was significant (F = 2.39, p < 0.05). Population number, hunting bag size and sampling effort did not correlate with the amount of roadkill (r = 0.23, 0.19 and 0.28 respectively, all NS; Figure 8).
The model for N. vison was significant (F3,15 = 21.3, p < 0.0001) and explained nearly 80% of roadkill variation (R2 = 0.77). Both the size of the hunting bag and sampling intensity were significant (F = 5.79 and F = 4.47, both p < 0.001), while AADT was not (F = 0.43, NS). Numbers of hunted N. vison correlated with the roadkill of the same year strongly and positively (r = 0.75, p < 0.001; Figure 8) and so did sampling intensity (r = 0.61, p < 0.001). L. lutra and N. vison, although about 10 and 5 times less numerous in terms of population than C. fiber, had relatively high levels of roadkill, these still amounting to about half of the C. fiber roadkill numbers. In O. zibethicus, the amount of registered roadkill was too small to make any comparisons.
We found the differences in the distances from the roadkill to the nearest water source to not be significant among semiaquatic mammal species, regardless of the scale (size) of the water source: CLC water, Kruskal-Wallis H3,92 = 1.37, p = 0.71; CLC wetland, H = 2.87, p = 0.41; GRPK river, H = 3.22, p = 0.36 and GRPK lake, H = 0.94, p = 0.81. Measured at the highest possible map scale, 26% of C. fiber roadkill occurred less than 50 m from the smallest possible water source, 43% less than 100 m and 62% less than 200 m from water. For L. lutra, the respective figures were 24%, 38% and 48%, while for N. vison they were 15%, 19% and 46%. In other words, 38–54% of roadkill in these species occurred over 200 m from the nearest water (Figure 9). Differences among species were not significant. Distances to large water sources, including wetlands, were much bigger. The average distances are presented in Table 3.

4. Discussion

Upon completing our analysis of roadkill of the four affected semiaquatic mammal species in Lithuania, we found roadkill to be a rare event. The proportion of C. fiber was 0.15% of all roadkill on average, with the proportions of N. vison and L. lutra being 2–3 times lower. Extrapolated from the roadkill index for the entire network of main and national roads of the country, the number of individuals killed annually is 44–357 for C. fiber, 36–456 for L. lutra, 49–464 for N. vison and 89–144 for O. zibethicus.
Otter roadkill is of particular interest as this species was decreasing for a long time in many western countries. In Britain, L. lutra roadkill amounted to 673 animals in 1971–1996 [54], these numbers increasing later, with 500 records in the southwest of the country alone between 1984 and 2004 [55]. In a 3000 sq. km area of northeastern Germany, there were 88 instances of L. lutra roadkill in 1990–2003 [56], while the total amount of L. lutra roadkill in eastern Germany was 746 ind. In the period 1957–1998 [57]. In the Czech Republic, there were 316 instances of L. lutra roadkill from 1990 to 2011 [58], while 53 ind. Were recorded in Hungary during 2006–2011 [59]. These relatively low numbers are in accordance with those found in Denmark [60], but 109 instances of otter roadkill in northern Israel during 1965–2004 represented an estimated 5% loss of the population each year [61]. Based on our data, the L. lutra roadkill numbers in Lithuania account for between 1% and 10% of the population annually, with data from the last three decades not showing any overall population decrease [12,46,48].
Higher traffic intensity is mentioned as a factor for increased L. lutra roadkill frequency [58], though our study did not confirm this. At present, neither spatial nor temporal correlations allow us to determine whether or not the number of otter casualties has had an impact on otter populations [55].
In general, the global trend of an increase in roadkill numbers over time is accepted, see references in [15,62], though during the COVID-19 lockdowns, there were decreases in roadkill, at least in some species [63]. In this context, the numbers of semiaquatic animal roadkill in various countries are very low. For various mammal species, low roadkill rates may be explained as due to the species being a habitat specialist [64]. In terms of semiaquatic species, it may be that aquatic and wetland habitats are not frequently around roads.
In Poland, a neighboring country of Lithuania, the proportion of O. zibethicus was 0.50% of total mammal roadkill in 2000–2016, while that of C. fiber and of N. vison was both 0.38% [65]. In the southwestern part of Poland in the period 2001–2003, no C. fiber roadkill was registered, while that of O. zibethicus was 0.52% and N. vison 0.26% out of 383 wild mammals [66]. In northeastern Poland, on the road crossing the valley of the large river Biebrza, O. zibethicus and N. vison roadkill each accounted for 1.25% out of 80 wild mammal registrations [67].
No semiaquatic mammal roadkill was registered in Croatia between 2005 and 2008 [68] or between 2007 and 2009 [69]. The same absence of semiaquatic mammal roadkill was characteristic to a study of 1175 instances of roadkill in Brittany, western France [70], 40,000 instance of roadkill in Czech Republic [27] and ca. 2500 roadkill registrations in Walonia, Belgium [71].
However, there were also cases with much higher proportions of semiaquatic animal roadkill. In the Republic of Ireland in 2008–2010, out of 548 roadkill instances, 15 instances were N. vison and 10 L. lutra, comprising 2.74% and 1.82%, respectively [72]. In areas where roads run in river valleys and wetlands, the proportion of O. zibethicus roadkill may reach much higher values. There are two investigations from North America that report very high proportions of O. zibethicus roadkill—10.4% in NY State [73] and 15.75% in California—in an area where a highway traverses a tidal marsh and diked baylands [74].
Our data show that 38–54% of semiaquatic mammal roadkill occurred more than 200 m from the nearest water. In this, we included the smallest possible water sources, such as land reclamation ditches, which are nearly or completely dry in the warm season. For L. lutra, such a phenomenon was known before. In Germany, between 1985 and 1993, 46.84% of otter roadkill was registered at sites with no adjacent waterbodies [75], and 32% were further than 200 m [56]. By contrast, 67% of all L. lutra casualty records occurred in a 100 m wide zone surrounding fresh water and the coast in Great Britain [54] and 57% in Israel [61]. Data about the other species have not been published before.
Of the other peculiarities of semiaquatic animal roadkill, it should be mentioned that these events are not representative of the population [76]; there are behavior differences in relation to oncoming vehicles that may lead to differences in their roadkill [77], and possible mitigations can create barrier effects for them [54,73]. It was shown that the survival of the American beaver (Castor canadensis) is inverse to habitat suitability; thus, a new approach might be required to understand semiaquatic mammal distribution and movements [78].
In Europe, N. vison densities vary from below one to over ten individuals per 10 km of water length [79]. Being a semiaquatic, generalist predator, N. vison can live in various habitats: in Greece, two thirds of records were near water, but many animals were also recorded far from water [80]. In central Italy, roadkill was the first evidence of species spread [81]. Avoiding urbanized areas and isolated ponds in the agricultural landscape is characteristic to the species [82]. During the dry season and droughts, N. vison readily uses terrestrial habitats, though the species is subjected to increased mortality when away from streams [83]. Under conditions of climate change, the drying of landscapes will affect local hydrology regimes, changing semiaquatic mammal population dynamics and movements [84]; therefore, the amount of roadkill registered not in proximity to waterbodies may further increase. This may apply to O. zibethicus as it is the semiaquatic mammal species most restricted to aquatic habitats [83]. Finally, habitat features may be not the main factors affecting occupancy by semiaquatic mammals, at least for the river otter (Lontra canadensis) and N. vison [85].
The fate of introduced and reintroduced semiaquatic species in Lithuania varied. Following the release from captivity in 1950 and the introduction in 1953, N. vison numbers continuously increased [10], totally suppressing the native European mink (Mustela lutreola) [11,14].
After the re-establishment and reintroduction of C. fiber to Lithuania in the 1940s and 1950s, they rapidly colonized the entire country [9,10]. According to some expert opinions, their numbers have been close to 100,000 individuals in recent decades [10], i.e., twice the official estimate (see Table 2). With the collapse of the fur market, these numbers are near the carrying capacity of the habitats.
O. zibethicus was introduced into eastern and southern parts of Lithuania in 1954, where it still remains most abundant. It is scarce in the north of the country [10,11]. Across the country, however, the species declined by about 94% in the period from 1989 to 2013 [86,87]. The success of the initial introduction is considered to be related to similarities to the climate and habitats within the native species range, as well as to the presence of a free ecological niche [86]. By contrast, the reasons for the decline are not well understood, and the presence of a high number of native predators may be involved, as may pathogens [87].
As N. vison and O. zibethicus are considered invasive species in the EU, while L. lutra is protected in many countries of Europe, it could be considered valuable to enhance registrations of their roadkill (using targeted efforts by drivers, hunters or other citizen scientists) in order to obtain the extrapolated amount of roadkill and to use this knowledge in species management.

5. Conclusions

Semiaquatic mammal roadkill is rare, even in C. fiber accounting for only 0.15% of total roadkill numbers. The national annual roadkill numbers were 44–357 in the case of C. fiber, 36–456 in L. lutra, 49–464 in N. vison and 89–144 in O. zibethicus. Roadkill does not necessarily occur in proximity to waterbodies or wetlands, with 38–54% occurring over 200 m from the nearest water sources.

Author Contributions

Conceptualization, L.B. (Linas Balčiauskas); methodology, all authors; investigation, L.B. (Linas Balčiauskas), L.B. (Laima Balčiauskienė) and J.S.; 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); writing—review and editing, all authors. All authors have read and agreed to the published version of the manuscript.

Funding

In 2019 and 2020, roadkill surveys were 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).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

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

Acknowledgments

We thank Deivis Kulvietis and Artūras Kajėnas, both from the Lithuanian Police Traffic Supervision Service, for their help with roadkill data.

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.

References

  1. Miljutin, A. Substrate utilization and feeding strategies of mammals: Description and classification. Est. J. Ecol. 2009, 58, 60–71. [Google Scholar] [CrossRef] [Green Version]
  2. Hood, G.A. Semi-Aquatic Mammals: Ecology and Biology; JHU Press: Baltimore, MD, USA, 2020. [Google Scholar]
  3. Ulevičius, A.; Balčiauskas, L. Spatial relations among semi-aquatic mammals on the riverside. Acta Zool. Litu. 1999, 9, 42–48. [Google Scholar] [CrossRef]
  4. Hood, G.A. Accessing and mobilizing “new” data to evaluate emerging environmental impacts on semi-aquatic mammals. Land 2020, 9, 345. [Google Scholar] [CrossRef]
  5. Jones, C.G.; Lawton, J.H.; Shachak, M. Organisms as ecosystem engineers. Oikos 1994, 69, 373–386. [Google Scholar] [CrossRef]
  6. Nummi, P.; Liao, W.; Huet, O.; Scarpulla, E.; Sundell, J. The beaver facilitates species richness and abundance of terrestrial and semi-aquatic mammals. Glob. Ecol. Cons. 2019, 20, e00701. [Google Scholar] [CrossRef]
  7. Mortensen, R.M.; Reinhardt, S.; Hjønnevåg, M.E.; Wilson, R.P.; Rosell, F. Aquatic habitat use in a semi-aquatic mammal: The Eurasian beaver. Anim. Biotelem. 2021, 9, 1–19. [Google Scholar] [CrossRef]
  8. Hoffer, D.M.; Nielsen, C.; Rutter, A.; Anile, S. Occupancy of Semi-Aquatic Mammals in an Urban Landscape; Research Square preprint; Southern Illinois University: Carbondale, IL, USA, 2021. [Google Scholar] [CrossRef]
  9. Ulevičius, A.; Kisielytė, N.; Jasiulionis, M. Habitat use and selectivity by beavers (Castor fiber) in anthropogenic landscape. Ekologija 2011, 57, 47–54. [Google Scholar] [CrossRef] [Green Version]
  10. Balčiauskas, L.; Trakimas, G.; Juškaitis, R.; Ulevičius, A.; Balčiauskienė, L. Atlas of Lithuanian Mammals, Amphibians and Reptiles, 2nd ed.; Akstis: Vilnius, Lithuania, 1999. [Google Scholar]
  11. Atlas of Lithuanian Mammals. Available online: https://gamtostyrimai.lt/en/users/viewGroup/id.57/pageId.23 (accessed on 10 February 2022).
  12. Baltrūnaitė, L.; Balčiauskas, L.; Matulaitis, R.; Stirkė, V. Otter distribution in Lithuania in 2008 and changes in the last decade. Est. J. Ecol. 2009, 58, 94–102. [Google Scholar] [CrossRef] [Green Version]
  13. Ulevičius, A.; Balčiauskas, L. Changes in the territory use by semi-aquatic mammals in some of rivers in Lithuania. Acta Zool. Litu. 2002, 12, 151–158. [Google Scholar] [CrossRef]
  14. Arbačiauskas, K.; Augutis, D.; Balčiauskas, L.; Bastytė-Cseh, D. Red Data Book of Lithuania. Animals, Plants, Fungi; Rašomavičius, V., Ed.; Lietuvos Respublikos Aplinkos Ministerija: Vilnius, Lietuva, 2021; pp. 1–684. [Google Scholar]
  15. Krukowicz, T.; Firląg, K.; Chrobot, P. Spatiotemporal Analysis of Road Crashes with Animals in Poland. Sustainability 2022, 14, 1253. [Google Scholar] [CrossRef]
  16. Balčiauskas, L.; Stratford, J.; Balčiauskienė, L.; Kučas, A. Importance of professional roadkill data in assessing diversity of mammal roadkills. Transp. Res. Part D Transp. Environ. 2020, 87, 102493. [Google Scholar] [CrossRef]
  17. González-Gallina, A.; Benítez-Badillo, G.; Rojas-Soto, O.R.; Hidalgo-Mihart, M.G. The small, the forgotten and the dead: Highway impact on vertebrates and its implications for mitigation strategies. Biodivers. Conserv. 2013, 22, 325–342. [Google Scholar] [CrossRef]
  18. Ortega, A.; Weiler, A. Diversity of vertebrates affected by roadkill and areas of greatest impact in Ruta III General Elizardo Aquino, Paraguay. Reportes Científicos de la FACEN 2018, 9, 31–41. [Google Scholar] [CrossRef]
  19. Abra, F.D.; da Costa Canena, A.; Garbino, G.S.T.; Medici, E.P. Use of unfenced highway underpasses by lowland tapirs and other medium and large mammals in central-western Brazil. Perspect. Ecol. Conserv. 2020, 18, 247–256. [Google Scholar] [CrossRef]
  20. Ferreguetti, A.C.; Graciano, J.M.; Luppi, A.P.; Pereira-Ribeiro, J.; Rocha, C.F.D.; Bergallo, H.G. Roadkill of medium to large mammals along a Brazilian road (BR-262) in Southeastern Brazil: Spatial distribution and seasonal variation. Stud. Neotrop. Fauna Environ. 2020, 55, 216–225. [Google Scholar] [CrossRef]
  21. Prist, P.R.; Garbino, G.S.; Abra, F.D.; Pagotto, T.; Giacon, O.O. Use of highway culverts by the water opossum (Chironectes minimus) in southeastern Brazil. Biota Neotrop. 2020, 20, e20201058. [Google Scholar] [CrossRef]
  22. 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]
  23. Schwartz, A.L.; Shilling, F.M.; Perkins, S.E. The value of monitoring wildlife roadkill. Eur. J. Wildl. Res. 2020, 66, 18. [Google Scholar] [CrossRef] [Green Version]
  24. Kučas, A.; Balčiauskas, L. Impact of Road Fencing on Ungulate-Vehicle Collisions and Hotspot Patterns. Land 2021, 10, 338. [Google Scholar] [CrossRef]
  25. Rodríguez-Morales, B.; Diaz-Varela, E.; Marey-Perez, M. Spatiotemporal Analysis of Vehicle Collisions Involving Wild Boar and Roe Deer in NW Spain. Accid. Anal. Prev. 2013, 60, 121–133. [Google Scholar] [CrossRef]
  26. D’Amico, M.; Roman, J.; de los Reyes, L.; Revilla, E. Vertebrate Road-Kill Patterns in Mediterranean Habitats: Who, When and Where. Biol. Conserv. 2015, 191, 234–242. [Google Scholar] [CrossRef]
  27. 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]
  28. Sullivan, J.M. Trends and Characteristics of Animal-Vehicle Collisions in the United States. J. Saf. Res. 2011, 42, 9–16. [Google Scholar] [CrossRef] [PubMed]
  29. Ascensão, F.; Yogui, D.; Alves, M.; Medici, E.P.; Desbiez, A. Predicting Spatiotemporal Patterns of Road Mortality for Medium-Large Mammals. J. Environ. Manag. 2019, 248, 109320. [Google Scholar] [CrossRef] [PubMed]
  30. Pagany, R. Wildlife-Vehicle Collisions—Influencing Factors, Data Collection and Research Methods. Biol. Conserv. 2020, 251, 108758. [Google Scholar] [CrossRef]
  31. Kušta, T.; Keken, Z.; Ježek, M.; Holá, M.; Šmíd, P. The Effect of Traffic Intensity and Animal Activity on Probability of Ungulate-Vehicle Collisions in the Czech Republic. Saf. Sci. 2017, 91, 105–113. [Google Scholar] [CrossRef]
  32. Bramati, P.S.; Heinert, L.F.; Narloch, L.B.; Hostetter, J.; Finkielman, J.D. Animal-Related Motorcycle Collisions in North Dakota. Wilderness Environ. Med. 2012, 23, 65–69. [Google Scholar] [CrossRef] [Green Version]
  33. Collinson, W.J.; Marneweck, C.; Davies-Mostert, H.T. Protecting the protected: Reducing wildlife roadkill in protected areas. Anim. Conserv. 2019, 22, 396–403. [Google Scholar] [CrossRef]
  34. 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. Glob. Ecol. Biogeogr. 2021, 30, 2200–2210. [Google Scholar] [CrossRef]
  35. Clevenger, A.; Chruszcz, B.; Gunson, K. Spatial Patterns and Factors Influencing Small Vertebrate Fauna Road-Kill Aggregations. Biol. Conserv. 2003, 109, 15–26. [Google Scholar] [CrossRef]
  36. Keken, Z.; Sedoník, J.; Kušta, T.; Andrášik, R.; Bíl, M. Roadside Vegetation Influences Clustering of Ungulate Vehicle Collisions. Transp. Res. Part D Transp. Environ. 2019, 73, 381–390. [Google Scholar] [CrossRef]
  37. Kučas, A.; Balčiauskas, L. Roadkill-Data-Based Identification and Ranking of Mammal Habitats. Land 2021, 10, 477. [Google Scholar] [CrossRef]
  38. Balčiauskas, L.; Stratford, J.; Balčiauskienė, L.; Kučas, A. Roadkills as a Method to Monitor Raccoon Dog Populations. Animals 2021, 11, 3147. [Google Scholar] [CrossRef]
  39. Colino-Rabanal, V.J.; Langen, T.A.; Peris, S.J.; Lizana, M. Ungulate: Vehicle Collision Rates Are Associated with the Phase of the Moon. Biodivers. Conserv. 2018, 27, 681–694. [Google Scholar] [CrossRef]
  40. Kučas, A.; Balčiauskas, L. Temporal Patterns of Ungulate-Vehicle Collisions in Lithuania. J. Environ. Manag. 2020, 273, 111172. [Google Scholar] [CrossRef]
  41. VĮ Lietuvos Automobilių Kelių Direkcija. Lietuvos Keliai. Available online: https://lakd.lrv.lt/lt/veiklos-sritys/lietuvos-keliai (accessed on 16 March 2021).
  42. VĮ Lietuvos Automobilių Kelių Direkcija. Eismo Apskaita Valstybinės Reikšmės Keliuose 2020 m. Available online: https://lakd.lrv.lt/uploads/lakd/documents/files/Eismo_intensyvumas/eismo_apskaita_valstybines_reiksmes_keliuose_2020_m.pdf (accessed on 16 March 2021).
  43. Jakimavičius, M. Analysis and Assessment of Lithuanian Road Accidents by AHP Method. Balt. J. Road Bridge Eng. 2018, 13, 238–260. [Google Scholar] [CrossRef]
  44. Brzeziński, M.; Romanowski, J.; Żmihorski, M.; Karpowicz, K. Muskrat (Ondatra zibethicus) decline after the expansion of American mink (Neovison vison) in Poland. Eur. J. Wildl. Res. 2010, 56, 341–348. [Google Scholar] [CrossRef]
  45. Lietuvos Respublikos Aplinkos Ministerija. Sumedžioti Žvėrys ir Paukščiai. Available online: https://am.lrv.lt/lt/veiklos-sritys-1/gamtos-apsauga/medziokle/ (accessed on 15 March 2022).
  46. Balčiauskas, L.; Ulevičius, A. Semi-aquatic mammal environment correlates in South Lithuanian river valleys. Ekologija 1995, 2, 37–44. [Google Scholar]
  47. Ulevicius, A.; Balciauskas, L. Ecology of semi-aquatic mammals in some landscapes of Lithuania: Abundance, community structure and distribution. Ekologija 1995, 2, 77–83. [Google Scholar]
  48. Balčiauskas, L.; Balčiauskienė, L.; Nature Research Centre, Vilnius, Lithuania. Personal Communication, 2022.
  49. Balčiauskas, L.; Nature Research Centre, Vilnius, Lithuania. Personal Communication, 2022.
  50. European Environmental Agency. CORINE Land Cover—Copernicus Land Monitoring Service [WWW Document]. 2018. Available online: https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=metadata (accessed on 15 March 2020).
  51. Brown, L.D.; Cat, T.T.; DasGupta, A. Interval Estimation for a proportion. Stat. Sci. 2001, 16, 101–133. [Google Scholar] [CrossRef]
  52. Dean, A.G.; Sullivan, K.M.; Soe, M.M. OpenEpi: Open Source Epidemiologic Statistics for Public Health. Available online: http://OpenEpi.com (accessed on 19 January 2021).
  53. G-Test Calculator. Available online: https://elem.com/~btilly/effective-ab-testing/g-test-calculator.html (accessed on 16 February 2021).
  54. Philcox, C.K.; Grogan, A.L.; Macdonald, D.W. Patterns of otter Lutra lutra road mortality in Britain. J. Appl. Ecol. 1999, 36, 748–762. [Google Scholar] [CrossRef]
  55. Chanin, P. Otter road casualties. Hystrix Ital. J. Mammal. 2006, 17, 79–90. [Google Scholar]
  56. Jancke, S.; Giere, P. Patterns of otter Lutra lutra road mortality in a landscape abundant in lakes. Eur. J. Wildl. Res. 2011, 57, 373–381. [Google Scholar] [CrossRef]
  57. Hauer, S.; Ansorge, H.; Zinke, O. Mortality patterns of otters (Lutra lutra) from eastern Germany. J. Zool. 2002, 256, 361–368. [Google Scholar] [CrossRef]
  58. Poledník, L.; Poledníková, K.; Větrovcová, J.; Hlaváč, V.; Beran, V. Causes of deaths of Lutra lutra in the Czech Republic (Carnivora: Mustelidae). Lynx Series Nova 2011, 42, 145–157. [Google Scholar]
  59. Cserkész, T.; Ottlecz, B.; Cserkész-Nagy, Á.; Farkas, J. Interchange as the main factor determining wildlife–vehicle collision hotspots on the fenced highways: Spatial analysis and applications. Eur. J. Wildl. Res. 2013, 59, 587–597. [Google Scholar] [CrossRef]
  60. Madsen, A.B. Otter Lutra lutra mortality in relation to traffic, and experience with newly established fauna passages at existing road bridges. Lutra 1996, 39, 76–90. [Google Scholar]
  61. Guter, A.; Dolev, A.; Saltz, D.; Kronfeld-Schor, N. Temporal and spatial influences on road mortality in otters: Conservation implications. Isr. J. Ecol. Evol. 2005, 51, 199–207. [Google Scholar] [CrossRef]
  62. Rendall, A.R.; Webb, V.; Sutherland, D.R.; White, J.G.; Renwick, L.; Cooke, R. Where wildlife and traffic collide: Roadkill rates change through time in a wildlife-tourism hotspot. Glob. Ecol. Conserv. 2021, 27, e01530. [Google Scholar] [CrossRef]
  63. 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]
  64. Barthelmess, E.L.; Brooks, M.S. The influence of body-size and diet on road-kill trends in mammals. Biodivers. Conserv. 2010, 19, 1611–1629. [Google Scholar] [CrossRef]
  65. Kustusch, K.; Wuczynski, A.; Smiertelnosc Zwierzat na Drogach w Polsce w Roku 2016. Raport Roczny “Ogólnopolskiego Rejestru Smiertelnosci Zwierzat na Drogach”. Available online: https://zwierzetanadrodze.pl/files/Zwierzeta_na_Drodze_-_Raport_2016.pdf (accessed on 3 November 2021).
  66. 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]
  67. Gryz, J.; Krauze, D. Mortality of vertebrates on a road crossing the Biebrza Valley (NE Poland). Eur. J. Wildl. Res. 2008, 54, 709–714. [Google Scholar] [CrossRef]
  68. Pintur, K.; Slijepčević, V.; Popović, N.; Andrijašević, D. Dynamics of wildlife-vehicle collisions on roads of karlovac county, Croatia. J. Cent. Eur. Agric. 2012, 13, 340–349. [Google Scholar] [CrossRef] [Green Version]
  69. Šprem, N.; Duduković, D.; Keros, T.; Konjević, D. Wildlife-vehicle collisions in Croatia—A hazard for humans and animals. Coll. Antropol. 2013, 37, 531–535. [Google Scholar] [PubMed]
  70. Guinard, E.; Billon, C.; Bretaud, F.; Sordello, R.; Chevallier, L.; Witté, I. Comparative study of animal carcass survey methods on roads. Transport. Res. D 2022. submitted. [Google Scholar] [CrossRef]
  71. 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]
  72. Haigh, A. Annual patterns of mammalian mortality on Irish roads. Hystrix Ital. J. Mammal. 2012, 23, 57–65. [Google Scholar] [CrossRef]
  73. Barthelmess, E.L. Spatial distribution of road-kills and factors influencing road mortality for mammals in Northern New York State. Biodivers. Conserv. 2014, 23, 2491–2514. [Google Scholar] [CrossRef]
  74. Winton, B.R.; Takekawa, J.Y. Transportation Impacts to Wildlife on State Route 37 in Northern San Pablo Bay, California. Trans. West. Sect. Wildl. Soc. 2001, 37, 55–60. [Google Scholar]
  75. Körbel, O. Hindering Otter Lutra lutra Road Kills Part 2. IUCN Otter Spec. Group Bull. 1995, 11, 32–36. [Google Scholar]
  76. Quiles Tundidor, P.; Ascensão, F.; D’Amico, M.; Revilla, E.; Barrientos, R. Are road-kills representative of wildlife community obtained from atlas data? Hystrix Ital. J. Mammal. 2021, 32, 89–94. [Google Scholar] [CrossRef]
  77. Brieger, F.; Kämmerle, J.L.; Hagen, R.; Suchant, R. Behavioural reactions to oncoming vehicles as a crucial aspect of wildlife-vehicle collision risk in three common wildlife species. Accid. Anal. Prev. 2022, 168, 106564. [Google Scholar] [CrossRef] [PubMed]
  78. Barela, I.; Burger, L.M.; Taylor, J.; Evans, K.O.; Ogawa, R.; McClintic, L.; Wang, G. Relationships between survival and habitat suitability of semi-aquatic mammals. Ecol. Evol. 2020, 10, 4867–4875. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  79. Zschille, J.; Stier, N.; Roth, M.; Berger, U. Dynamics in space use of American mink (Neovison vison) in a fishpond area in Northern Germany. Eur. J. Wildl. Res. 2012, 58, 955–968. [Google Scholar] [CrossRef]
  80. Galanaki, A.; Kominos, T. The distribution of American mink (Neovison vison) in Greece. Mammalia 2022, 86, 57–65. [Google Scholar] [CrossRef]
  81. Mori, E.; Bini, A.; Viviano, A.; Bartolommei, P.; Mazza, G. Distribution of introduced American mink in the Northern Apennine area (Central Italy). Mammalia 2022. [Google Scholar] [CrossRef]
  82. Brzeziński, M.; Ignatiuk, P.; Żmihorski, M.; Zalewski, A. An invasive predator affects habitat use by native prey: American mink and water vole co-existence in riparian habitats. J. Zool. 2018, 304, 109–116. [Google Scholar] [CrossRef]
  83. Ahlers, A.A.; Cotner, L.A.; Wolff, P.J.; Mitchell, M.A.; Heske, E.J.; Schooley, R.L. Summer precipitation predicts spatial distributions of semiaquatic mammals. PLoS ONE 2015, 10, e0135036. [Google Scholar] [CrossRef] [Green Version]
  84. Ward, E.M.; Wysong, K.; Gorelick, S.M. Drying landscape and interannual herbivory-driven habitat degradation control semiaquatic mammal population dynamics. Ecohydrology 2020, 13, e2169. [Google Scholar] [CrossRef]
  85. Holland, A.M.; Schauber, E.M.; Nielsen, C.K.; Hellgren, E.C. River otter and mink occupancy dynamics in riparian systems. J. Wildl. Manag. 2019, 83, 1552–1564. [Google Scholar] [CrossRef] [Green Version]
  86. Skyriene, G.; Paulauskas, A. Distribution of invasive muskrats (Ondatra zibethicus) and impact on ecosystem. Ekologija 2012, 58, 357–367. [Google Scholar] [CrossRef] [Green Version]
  87. Skyrienė, G.; Paulauskas, A.; Ulevičius, A.; Samas, A. Decline of invasive muskrat in Lithuania. Весmник Oxomoведения 2014, 11, 291–294. [Google Scholar]
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Figure 1. Road network of Lithuania. Main roads are denoted by the numbers A1–A18; road sections with wildlife fences are shown in bold, and locations of mammal-collisions in 2002–2021 are indicated by dots.
Figure 1. Road network of Lithuania. Main roads are denoted by the numbers A1–A18; road sections with wildlife fences are shown in bold, and locations of mammal-collisions in 2002–2021 are indicated by dots.
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Figure 2. Seasonal distribution of semi-aquatic mammal roadkills, 2002–2021.
Figure 2. Seasonal distribution of semi-aquatic mammal roadkills, 2002–2021.
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Figure 3. Spatial distribution of the registered semiaquatic mammal roadkill in Lithuania, 2002–2021: (a) C. fiber, (b) L. lutra, (c) N. vison, (d) O. zibethicus.
Figure 3. Spatial distribution of the registered semiaquatic mammal roadkill in Lithuania, 2002–2021: (a) C. fiber, (b) L. lutra, (c) N. vison, (d) O. zibethicus.
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Figure 4. Temporal distribution of the registered semiaquatic mammal roadkill in Lithuania, 2002–2021.
Figure 4. Temporal distribution of the registered semiaquatic mammal roadkill in Lithuania, 2002–2021.
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Figure 5. Predictions of unregistered annual semiaquatic mammal roadkill in Lithuania, 2002–2021.
Figure 5. Predictions of unregistered annual semiaquatic mammal roadkill in Lithuania, 2002–2021.
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Figure 6. Relationship between registered C. fiber, L. lutra and N. vison roadkill numbers and sampling effort, expressed as the number of registration sessions. Dashed lines show 95% CI, not shown for nonsignificant correlation. Blue dots show registered roadkills.
Figure 6. Relationship between registered C. fiber, L. lutra and N. vison roadkill numbers and sampling effort, expressed as the number of registration sessions. Dashed lines show 95% CI, not shown for nonsignificant correlation. Blue dots show registered roadkills.
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Figure 7. Relationship between registered C. fiber, L. lutra and N. vison roadkill numbers and traffic intensity. Dashed lines show 95% CI, not shown for nonsignificant correlation. Blue dots show registered roadkills.
Figure 7. Relationship between registered C. fiber, L. lutra and N. vison roadkill numbers and traffic intensity. Dashed lines show 95% CI, not shown for nonsignificant correlation. Blue dots show registered roadkills.
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Figure 8. Relationship among registered C. fiber and N. vison roadkill numbers, population and hunting bag sizes. Dashed lines show 95% CI, not shown for nonsignificant correlation. Blue dots show registered roadkills.
Figure 8. Relationship among registered C. fiber and N. vison roadkill numbers, population and hunting bag sizes. Dashed lines show 95% CI, not shown for nonsignificant correlation. Blue dots show registered roadkills.
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Figure 9. Distribution of the registered semiaquatic mammal roadkill in Lithuania in relation to the water sources.
Figure 9. Distribution of the registered semiaquatic mammal roadkill in Lithuania in relation to the water sources.
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Table
Table 1. Population and hunting bag of semiaquatic mammals in Lithuania, 2003–2021 (n/a–data not available).
Table 1. Population and hunting bag of semiaquatic mammals in Lithuania, 2003–2021 (n/a–data not available).
YearBeaver (C. fiber)American Mink (N. vison)Muskrat (O. zibethicus)
PopulationHunting BagPopulationHunting BagPopulationHunting Bag
200311,0125630n/a66n/a75
200420,9265890n/a92n/a31
2005n/a4713n/a87n/a21
200623,7785359n/a95n/a105
200731,16012,473n/a110n/a79
200836,37512,702n/a109n/a60
200940,22213,593n/a95n/a72
201047,70216,231n/a107n/a30
201148,04618,821n/a82n/a72
201248,60420,591n/a148n/a231
201349,44611,778n/a153n/a148
201444,41621,749n/a281n/a329
201543,80219,544n/a166n/a99
201640,61819,293n/a97n/a76
201740,50617,503n/a81n/a73
201842,39615,637n/a59n/a60
201943,14819,977n/a106n/a42
202043,55119,907n/a46n/a39
202143,35515,651n/a53n/a23
Table
Table 2. Amount of wild mammal roadkill in Lithuania, 2002–2021, in decreasing order. Semiquatic mammal species shown in bold. Species with roadkill numbers less than 20 are pooled.
Table 2. Amount of wild mammal roadkill in Lithuania, 2002–2021, in decreasing order. Semiquatic mammal species shown in bold. Species with roadkill numbers less than 20 are pooled.
SpeciesN
Roe deer (Capreolus capreolus)25,583
Moose (Alces alces)2457
Wild boar (Sus scrofa)2027
Raccoon dog (Nyctereutes procyonoides) nn1659
Red fox (Vulpes vulpes)1175
Eastern European hedgehog (Erinaceus roumanicus)1158
European hare (Lepus europaeus)739
Red deer (Cervus elaphus)633
Marten (Martes sp.)413
Badger (Meles meles)203
European polecat (Mustela putorius)184
Pine marten (Martes martes)70
Beaver (Castor fiber)60
Stone marten (Martes foina)35
Red squirrel (Sciurus vulgaris)40
Fallow deer (Dama dama) nn45
American mink (Neovison vison)nn26
Grey wolf (Canis lupus)25
Eurasian otter (Lutra lutra)22
European mole (Talpa europaea)20
Muskrat (Ondatra zibethicus)nn3
Unknown 3313
Other species 46
Total, N 39,936
nn Non-native species; Other species included European bison (Bison bonasus), lynx (Lynx lynx), raccoon (Procyon lotor) nn, least weasel (Mustela nivalis), stoat (Mustela erminea), mountain hare (Lepus timidus), black rat (Rattus rattus), Norway rat (Rattus norvegicus) nn, bank vole (Clethrionomys glareolus), yellow-necked mouse (Apodemus flavicollis), common shrew (Sorex araneus) and water shrew (Neomys fodiens).
Table
Table 3. Distances among semiaquatic mammal roadkill and the nearest waterbodies or wetlands, presented as average ±SE; min–max (in m). CLC: Corine land cover (1:50,000), GRPK: base cadastre (1:10,000).
Table 3. Distances among semiaquatic mammal roadkill and the nearest waterbodies or wetlands, presented as average ±SE; min–max (in m). CLC: Corine land cover (1:50,000), GRPK: base cadastre (1:10,000).
C. fiberL. lutraN. visonO. zibethicus
CLC water2823 ± 371
0–11,310		2563 ± 619
159–10,450		2343 ± 389
338–9704		2376 ± 1085
229–3719
CLC wetland7067 ± 782
296–21,824		6503 ± 1173
267–20,608		5434 ± 1056
719–27,233		7372 ± 4695
159–16,185
GPRK river303 ± 101
6–4256		284 ± 63
13–1059		324 ± 64
13–1388		90 ± 10
73–106
GPRK lake1093 ± 156
13–4116		1260 ± 203
179–2961		1108 ± 193
114–4100		901 ± 547
229–1984
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MDPI and ACS Style

Balčiauskas, L.; Stratford, J.; Kučas, A.; Balčiauskienė, L. Factors Affecting Roadkills of Semi-Aquatic Mammals. Biology 2022, 11, 748. https://doi.org/10.3390/biology11050748

AMA Style

Balčiauskas L, Stratford J, Kučas A, Balčiauskienė L. Factors Affecting Roadkills of Semi-Aquatic Mammals. Biology. 2022; 11(5):748. https://doi.org/10.3390/biology11050748

Chicago/Turabian Style

Balčiauskas, Linas, Jos Stratford, Andrius Kučas, and Laima Balčiauskienė. 2022. "Factors Affecting Roadkills of Semi-Aquatic Mammals" Biology 11, no. 5: 748. https://doi.org/10.3390/biology11050748

APA Style

Balčiauskas, L., Stratford, J., Kučas, A., & Balčiauskienė, L. (2022). Factors Affecting Roadkills of Semi-Aquatic Mammals. Biology, 11(5), 748. https://doi.org/10.3390/biology11050748

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