Factors Affecting Wildlife–Vehicle Collisions: Comparison
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Please note this is a comparison between Version 4 by Catherine Yang and Version 3 by Haotong Su.

Wildlife–VCs ehicle Collisions (WVCs) are the most obvious negative effect of roads on wildlife. Scientists have estimated that approximately 194 million birds and 29 million mammals may be killed on roads in Europe each year [11]. WVCs have serious consequences for wildlife populations. Road mortality can be a primary cause of death for some species in some regions [2,12–15],, can reduce species abundance near roads [16–18], can limit genetic diversity [19], and can pose extinction threats to certain wildlife [20]. Identifying the influencing factors and summarizing the spatial-temporal patterns of WVCs have been major research trends in recent decades and are of great importance for mitigation measures.

  • wildlife–vehicle collisions (WVCs)
  • crossing willingness
  • crossing avoidance

1. Species Characteristics

Species characteristics affecting WVCs include the nearby population density, the crossing willingness or entering opportunity, the crossing ability, and morphological and life-history traits. Morphological and life-history traits include mobility, body size, diet type, gender, age, health, body length, group size, various road uses (e.g., foraging or thermoregulation on roads), reproductive and breeding behaviors, seasonal migrations, post-breeding dispersal, and movements to hibernation locations.
 
Numerous studies have found that the local population density is a crucial factor influencing WVCs [86,87,88,89,90,91][1][2][3][4][5][6]. As mentioned previously, larger species and carnivores are likely to be more mobile, but the specific effects on WVCs must be considered in combination with other factors. Several studies indicate that medium-sized mammals have the highest risk of WVCs, and the comparatively lower risk of WVCs of larger mammals can be explained by a lower population density, better crossing ability, and higher visibility for drivers [85,92,95,98][7][8][9][10]. Regarding birds, Møller et al. (2011) and Medrano-Vizcaíno et al. (2022) found that the WVCs of birds are positively correlated with body mass [84[7][11],85], while Morelli et al. (2020) came to the opposite conclusion [100][12]. There is also no consistent conclusion about the relationship between diet type and the risk of WVCs for mammals, as the dominant factors considered in different studies [80,92,95,96[8][9][10][13][14][15],98,99], such as mobility, population density, and foraging on roads, were not identical. The gender difference in the WVCs of certain species mainly results from the different crossing frequencies between males and females (bias toward males: e.g., Refs. [29,31,33][16][17][18]; bias toward females: e.g., Refs. [34][19]). Crossing roads in groups may reduce the risk of WVCs because of increased vigilance [96][14]. Nevertheless, Pfeiffer et al. (2020) pointed out that when white-tailed deer (Odocoileus virginianus) cross roads in groups, despite the increased total vigilance of the group, the vigilance of individuals in the group might decrease [101][20]. In addition, individuals in large groups tend to catch up with others on opposite sides of roads, thus increasing the risk of WVCs [93][21]. As a rare study involving the relationship between health conditions and WVCs, Møller et al. (2011) found that the risk of WVCs is higher in bird species with blood parasite infections [84][11]. Various road uses, such as foraging (e.g., predation, scavenging), movement, and thermoregulation, can increase the density and road crossing of wildlife, which may enhance the risk of WVCs [2,5][22][23]. For example, the presence of prey on roadsides may increase the risk of WVCs of predators [94,97][24][25]. In northern latitudes, just after dawn, birds often sit on tarmac roads, which are warmer than dew-laden roadside vegetation.

2. Road and Traffic Characteristics

Road and traffic characteristics affecting WVCs include road design, road age, traffic volume, and vehicle speed. Road design includes the road class, road width, road curvature, road embankments, road verges, medians, road lights, roadside vegetation, and mitigation measures.
Wider roads mean spending more time crossing, a higher traffic volume means fewer intervals to cross, and a higher vehicle speed means less time to react [62,115,116]. Although curves may lower the visibility of drivers [103], they may also reduce vehicle speed [60,128] and enhance drivers’ attention [121]. Indeed, the majority of studies indicate that WVCs are positively correlated with road width, number of lanes, road class, traffic volume, and speed limit and are negatively correlated with road curvature [21]. Conversely, a high traffic volume can reduce the crossing willingness of some species and lower the risk of WVCs [58,60,62,88,134,135,136,137]. Additionally, it should not be overlooked that high traffic may depress the nearby population density via high road mortality, which causes road-kill hotspots to move to low-traffic segments [55,138]. Furthermore, high traffic may also make corpses disappear faster [55]. The negative relationship between the road width and the WVCs of certain species can be primarily attributed to the crossing avoidance of wide roads [118,123]. Husby (2016) conducted a rare study demonstrating a negative relationship between vehicle speed and the WVCs of birds, and the interpretation was that the sound of high-speed vehicles might alert birds earlier or that these vehicles might hit birds harder and throw them further off roads without detection [139].Wider roads mean spending more time crossing, a higher traffic volume means fewer intervals to cross, and a higher vehicle speed means less time to react [26][27][28]. Although curves may lower the visibility of drivers [29], they may also reduce vehicle speed [30][31] and enhance drivers’ attention [32]. Indeed, the majority of studies indicate that WVCs are positively correlated with road width, number of lanes, road class, traffic volume, and speed limit and are negatively correlated with road curvature [33]. Conversely, a high traffic volume can reduce the crossing willingness of some species and lower the risk of WVCs [3][26][30][34][35][36][37][38]. Additionally, it should not be overlooked that high traffic may depress the nearby population density via high road mortality, which causes road-kill hotspots to move to low-traffic segments [39][40]. Furthermore, high traffic may also make corpses disappear faster [39]. The negative relationship between the road width and the WVCs of certain species can be primarily attributed to the crossing avoidance of wide roads [41][42]. Husby (2016) conducted a rare study demonstrating a negative relationship between vehicle speed and the WVCs of birds, and the interpretation was that the sound of high-speed vehicles might alert birds earlier or that these vehicles might hit birds harder and throw them further off roads without detection [43].
Raised roads or roadsides with high embankments decrease the risk of WVCs of many species [68,104[31][44][45],128], but this may not be the case for birds [83][46]. Lao et al. (2011) and Valero et al. (2015) found that an increased shoulder width increases the likelihood of WVCs of some species [109,119][47][48]. Vegetated medians increase the WVCs of a number of species because they may attract species for food or protection or may reduce the width of the gap for crossing [68,128,131][31][44][49]. Even rigid median barriers may enhance the risk of WVCs by trapping wildlife on roads [113][50]. Road lights attract some species while repelling others [62][26]. Those who are attracted to roads by artificial lights may have a higher risk of WVCs [110,112,129,130][51][52][53][54]. Moreover, vehicle headlights may dazzle some nocturnal birds [83,128][31][46] or may cause some species to freeze, such as possums (Trichosurus vulpecula) and hedgehogs [55][39].
Roadside vegetation can be an attractive habitat to a wide range of wildlife or can act as a corridor for movement. A large number of species prefer to cross roads at sites hidden by vegetation cover, which may limit the awareness of drivers or wildlife [35,39,82,99,104,108,115,126][15][27][45][55][56][57][58][59]. Actually, the majority of studies show a positive correlation between roadside vegetation and WVCs [21][33]. In detail, the structure of roadside vegetation may also have an impact. For example, dense vegetation may force birds to fly higher and decrease the risk of WVCs [106,128][31][60]. When incubating birds nesting in low roadside vegetation flush from their nests, they often fly low over the open road and are very vulnerable to WVCs. Galantinbo et al. (2022) found that wood mice (Apodemus sylvaticus) are more likely to cross roads near taller shrubs or after firebreak openings [132][61].
Road fences are a very effective mitigation measure to reduce WVCs [40,102,105,111,120,122,124,133][62][63][64][65][66][67][68][69]. Moreover, WVCs are likely to be concentrated at fence ends because of the funnel effect of fences [40,102,122,125,131][49][62][63][67][70]. Similarly, Cserkész et al. (2013) noted the high rate of WVCs near crossing structures due to fence gaps [114][71]. Fences may also become traps for wildlife, resulting from bad design or maintenance [127][72]. For instance, some species can climb over or pass through fences and get trapped on roads [107,117][73][74].
As mentioned previously, species may improve their crossing ability with time. The road age may correlate with the change in local abundance or the interaction behavior (e.g., habituation) with roads [72][75].

3. Landscape and Environmental Characteristics

Landscape and environmental characteristics affecting WVCs include the surrounding habitat and landscape, topography, weather, time of day, day of the week, and month of the year.
 
The likelihood of WVCs increases when roads intersect suitable habitats [118,129,144][41][53][76]. Most studies indicate that the amount and proximity of surrounding habitats (e.g., forest, grassland, wetland, water areas, agricultural land) are positively correlated with WVCs [10,21,126,135,140][33][36][59][77][78]. Furthermore, many studies have posited that the diversity of landscapes or habitat types may prompt the movement of wildlife and increase WVCs [21,104,108][33][45][58]. However, Puig et al. (2012) found that homogeneous landscapes on both sides of roads could increase WVCs of many medium-sized mammals due to higher crossing attempts, while heterogeneous landscapes were found to reduce WVCs [146][79]. Several studies indicate that the presence of national parks nearby raises the risk of WVCs [21][33], which is due to high species abundance in protected areas [80][13] or high traffic caused by tourism [7][80]. There is no major consensus on the correlation between developed areas and the risk of WVCs [21][33]. The explanations supporting a positive correlation include a higher abundance of some species attracted by exploiting anthropogenic resources, more nervous behavior, higher traffic density, and lower driver awareness in developed areas [60,80,130,136,141,142,145,147][13][30][37][54][81][82][83][84]. The explanations supporting a negative correlation mainly include the low density of some species in built areas due to the avoidance of human disturbance [107,143,144][73][76][85]. When linear topographies or landscapes (e.g., riparian structures, ditches, drainages, slopes, ridges) funnel wildlife to roads, they may increase the risk of WVCs [135,148][36][86]. The relationships between the slope or height of the surrounding terrain and WVCs seem to be complex [21,35,57,149][33][55][87][88].
Various climatic factors concerning WVCs have been discussed in relevant studies, including the temperature, humidity, precipitation, snow cover, wind, barometric pressure, fog, drought, extreme weather, photoperiod, and moon phase (e.g., Refs. [88,103,112,150,151,152,153,154,155,156][3][29][52][89][90][91][92][93][94][95]). For instance, Dussault et al. (2006) found more WVCs of moose during days with high temperatures and atmospheric pressure, possibly due to increased nocturnal activity or seeking open areas to avoid biting insects [150][89].
The seasonal distribution and the time distribution of the day of WVCs are mainly influenced by the activity patterns of wildlife. The day distribution of the week of WVCs is mainly influenced by the traffic volume. A large number of studies have demonstrated that seasonal peaks of road crossing and WVCs are consistent with seasonal life-history patterns [9,68,93,130,136,151,157,159,160][21][37][44][54][90][96][97][98][99]. Furthermore, numerous studies have found that the WVCs of some wildlife, such as ungulates and red foxes, are higher in dark periods because these species are more active during the night, and the WVCs are intensified by poor driver visibility [93,150,158,159,162,163][21][89][98][100][101][102]. Many studies have found that WVCs are more frequent on weekends due to higher traffic [60,93,150,159][21][30][89][98]. Similarly, WVCs can be more numerous on holidays because of a higher traffic volume [161][103].

4. Driver-Related Factors and Specific Human Activities

The driver-related factors affecting WVCs include visibility, attention, reaction, and attitude. Driver-related factors usually correspond to some other factors. For example, the visibility of drivers can be affected by the body size and color of wildlife, the driving speed, the road curvature, roadside vegetation, the weather, and the time of day. Moreover, the attention of drivers may decrease on straight roads [164][104] or at certain periods of time. The reaction of drivers is related to their visibility, attention, driving skill, driving speed, vehicle type, and the predictability of the crossing behavior of wildlife. The attitudes of drivers toward WVCs may vary with the species, and drivers may even hit certain species (e.g., snakes) intentionally [165,166,167,168][105][106][107][108]. Some human activities in certain periods of the year, such as hunting, may promote the activity of some wildlife and increase the risk of WVCs [10,60,129,159][30][53][77][98].

References

  1. Caceres, N.C. Biological characteristics influence mammal road kill in an Atlantic Forest-Cerrado interface in south-western Brazil. Ital. J. Zool. 2011, 78, 379–389.
  2. Orłowski, G. Spatial distribution and seasonal pattern in road mortality of the common toad Bufo bufo in an agricultural landscape of south-western Poland. Amphib. Reptil. 2007, 28, 25–31.
  3. Rolandsen, C.M.; Solberg, E.J.; Herfindal, I.; Van Moorter, B.; Sæther, B. Large-scale spatiotemporal variation in road mortality of moose: Is it all about population density? Ecosphere 2011, 2, 113.
  4. D’Amico, M.; Román, J.; de los Reyes, L.; Revilla, E. Vertebrate road-kill patterns in Mediterranean habitats: Who, when and where. Biol. Conserv. 2015, 191, 234–242.
  5. Ascensão, F.; Desbiez, A.L.J.; Medici, E.P.; Bager, A. Spatial patterns of road mortality of medium-large mammals in Mato Grosso do Sul, Brazil. Wildl. Res. 2017, 44, 135–146.
  6. Saint-Andrieux, C.; Calenge, C.; Bonenfant, C. Comparison of environmental, biological and anthropogenic causes of wildlife-vehicle collisions among three large herbivore species. Popul. Ecol. 2020, 62, 64–79.
  7. Medrano-Vizcaíno, P.; Grilo, C.; Pinto, F.A.S.; Carvalho, W.D.; Melinski, R.D.; Schultz, E.D.; González-Suárez, M. Roadkill patterns in Latin American birds and mammals. Glob. Ecol. Biogeogr. 2022, 31, 1756–1783.
  8. Ford, A.T.; Fahrig, L. Diet and body size of North American mammal road mortalities. Transp. Res. Part D Transp. Environ. 2007, 12, 498–505.
  9. 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.
  10. Hill, J.E.; DeVault, T.L.; Belant, J.L. Research note: A 50-year increase in vehicle mortality of North American mammals. Landsc. Urban Plan. 2020, 197, 103746.
  11. Møller, A.P.; Erritzøe, H.; Erritzøe, J. A behavioral ecology approach to traffic accidents: Interspecific variation in causes of traffic casualties among birds. Zool. Res. 2011, 32, 115–127.
  12. Morelli, F.; Rodríguez, R.A.; Benedetti, Y.; Delgado, J.D. Avian roadkills occur regardless of bird evolutionary uniqueness across Europe. Transp. Res. Part D Transp. Environ. 2020, 87, 102531.
  13. Akrim, F.; Mahmood, T.; Andleeb, S.; Hussain, R.; Collinson, W.J. Spatiotemporal patterns of wildlife road mortality in the Pothwar Plateau, Pakistan. Mammalia 2019, 83, 487–495.
  14. Cook, T.C.; Blumstein, D.T. The omnivore’s dilemma: Diet explains variation in vulnerability to vehicle collision mortality. Biol. Conserv. 2013, 167, 310–315.
  15. Jamhuri, J.; Edinoor, M.A.; Kamarudin, N.; Lechner, A.M.; Ashton-Butt, A.; Azhar, B. Higher mortality rates for large- and medium-sized mammals on plantation roads compared to highways in Peninsular Malaysia. Ecol. Evol. 2020, 10, 12049–12058.
  16. Schwab, A.C.; Zandbergen, P.A. Vehicle-related mortality and road crossing behavior of the Florida panther. Appl. Geogr. 2011, 31, 859–870.
  17. Poessel, S.A.; Burdett, C.L.; Boydston, E.E.; Lyren, L.M.; Alonso, R.S.; Fisher, R.N.; Crooks, K.R. Roads influence movement and home ranges of a fragmentation-sensitive carnivore, the bobcat, in an urban landscape. Biol. Conserv. 2014, 180, 224–232.
  18. Moore, L.J.; Petrovan, S.O.; Baker, P.J.; Bates, A.J.; Hicks, H.L.; Perkins, S.E.; Yarnell, R.W. Impacts and potential mitigation of road mortality for hedgehogs in Europe. Animals 2020, 10, 1523.
  19. Steen, D.A.; Aresco, M.J.; Beilke, S.G.; Compton, B.W.; Condon, E.P.; Dodd, C.K.; Forrester, H.; Gibbons, J.W.; Greene, J.L.; Johnson, G.; et al. Relative vulnerability of female turtles to road mortality. Anim. Conserv. 2006, 9, 269–273.
  20. Pfeiffer, M.B.; Iglay, R.B.; Seamans, T.W.; Blackwell, B.F.; DeVault, T.L. Deciphering interactions between white-tailed deer and approaching vehicles. Transp. Res. Part D Transp. Environ. 2020, 79, 102251.
  21. Eloff, P.; van Niekerk, A. Temporal patterns of animal-related traffic accidents in the Eastern Cape, South Africa. S. Afr. J. Wildl. Res. 2008, 38, 153–162.
  22. Forman, R.T.T.; Sperling, D.; Bissonette, J.A.; Clevenger, A.P.; Cutshall, C.D.; Dale, V.H.; Fahrig, L.; France, R.; Goldman, C.R.; Heanue, K.; et al. Road Ecology: Science and Solutions; Island Press: Washington, DC, USA, 2003.
  23. Hill, J.E.; De Vault, T.L.; Belant, J.L. A review of ecological factors promoting road use by mammals. Mammal Rev. 2021, 51, 214–227.
  24. Barrientos, R.; Bolonio, L. The presence of rabbits adjacent to roads increases polecat road mortality. Biodivers. Conserv. 2009, 18, 405–418.
  25. Planillo, A.; Mata, C.; Manica, A.; Malo, J.E. Carnivore abundance near motorways related to prey and roadkills. J. Wildl. Manag. 2018, 82, 319–327.
  26. van Langevelde, F.; Jaarsma, C.F. Using traffic flow theory to model traffic mortality in mammals. Landsc. Ecol. 2004, 19, 895–907.
  27. 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.
  28. Meisingset, E.L.; Loe, L.E.; Brekkum, Ø.; Mysterud, A. Targeting mitigation efforts: The role of speed limit and road edge clearance for deer-vehicle collisions. J. Wildl. Manag. 2014, 78, 679–688.
  29. Lee, E.; Klöcker, U.; Croft, D.B.; Ramp, D. Kangaroo-vehicle collisions in Australia’s sheep rangelands, during and following drought periods. Aust. Mammal. 2004, 26, 215–226.
  30. Zuberogoitia, I.; del Real, J.; Torres, J.J.; Rodríguez, L.; Alonso, M.; Zabala, J. Ungulate vehicle collisions in a peri-urban environment: Consequences of transportation infrastructures planned assuming the absence of ungulates. PLoS ONE 2014, 9, e107713.
  31. Canal, D.; Camacho, C.; Martin, B.; de Lucas, M.; Ferrer, M. Fine-scale determinants of vertebrate roadkills across a biodiversity hotspot in Southern Spain. Biodivers. Conserv. 2019, 28, 3239–3256.
  32. Ranapurwala, S.I.; Mello, E.R.; Ramirez, M.R. A GIS-based matched case-control study of road characteristics in farm vehicle crashes. Epidemiology 2016, 27, 827–834.
  33. Pagany, R. Wildlife-vehicle collisions—Influencing factors, data collection and research methods. Biol. Conserv. 2020, 251, 108758.
  34. Grilo, C.; Ferreira, F.Z.; Revilla, E. No evidence of a threshold in traffic volume affecting road-kill mortality at a large spatio-temporal scale. Environ. Impact Assess. Rev. 2015, 55, 54–58.
  35. Mazerolle, M.J. Amphibian road mortality in response to nightly variations in traffic intensity. Herpetologica 2004, 60, 45–53.
  36. Seiler, A. Predicting locations of moose–vehicle collisions in Sweden. J. Appl. Ecol. 2005, 42, 371–382.
  37. Grilo, C.; Bissonette, J.A.; Santos-Reis, M. Spatial-temporal patterns in Mediterranean carnivore road casualties: Consequences for mitigation. Biol. Conserv. 2009, 142, 301–313.
  38. Kušta, T.; Keken, Z.; Ježek, M.; Holá, M.; Šmid, 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.
  39. Brockie, R.E.; Sadleir, R.M.F.S.; Linklater, W.L. Long-term wildlife road-kill counts in New Zealand. N. Z. J. Zool. 2009, 36, 123–134.
  40. Teixeira, F.Z.; Kindel, A.; Hartz, S.M.; Mitchell, S.; Fahrig, L. When road-kill hotspots do not indicate the best sites for road-kill mitigation. J. Appl. Ecol. 2017, 54, 1544–1551.
  41. Gagné, S.A.; Bates, J.L.; Bierregaard, R.O. The effects of road and landscape characteristics on the likelihood of a Barred Owl (Strix varia)-vehicle collision. Urban Ecosyst. 2015, 18, 1007–1020.
  42. Bitušík, P.; Kocianová-Adamcová, M.; Brabec, J.; Malina, R.; Tesák, J.; Urban, P. The effects of landscape structure and road topography on mortality of mammals: A case study of two different road types in Central Slovakia. Lynx Ser. Nova 2017, 48, 39–51.
  43. Husby, M. Factors affecting road mortality in birds. Ornis Fenn. 2016, 93, 212–224.
  44. Clevenger, A.P.; Chruszczc, B.; Gunson, K.E. Spatial patterns and factors influencing small vertebrate fauna road-kill aggregations. Biol. Conserv. 2003, 109, 15–26.
  45. Malo, J.E.; Suárez, F.; Díez, A. Can we mitigate animal-vehicle accidents using predictive models? J. Appl. Ecol. 2004, 41, 701–710.
  46. Erritzoe, J.; Mazgajski, T.D.; Rejt, L. Bird casualties on European roads—A review. Acta Ornithol. 2003, 38, 77–93.
  47. Lao, Y.; Wu, Y.; Corey, J.; Wang, Y. Modeling animal-vehicle collisions using diagonal inflated bivariate Poisson regression. Accid. Anal. Prev. 2011, 43, 220–227.
  48. Valero, E.; Picos, J.; Álvarez, X. Road and traffic factors correlated to wildlife–vehicle collisions in Galicia (Spain). Wildl. Res. 2015, 42, 25–34.
  49. Plante, J.; Jaeger, J.A.G.; Desrochers, A. How do landscape context and fences influence roadkill locations of small and medium-sized mammals? J. Environ. Manag. 2019, 235, 511–520.
  50. Clevenger, A.P.; Kociolek, A.V. Potential impacts of highway median barriers on wildlife: State of the practice and gap analysis. Environ. Manag. 2013, 52, 1299–1312.
  51. Seshadri, K.S.; Ganesh, T. Faunal mortality on roads due to religious tourism across time and space in protected areas: A case study from south India. For. Ecol. Manag. 2011, 262, 1713–1721.
  52. Coelho, I.P.; Teixeira, F.Z.; Colombo, P.; Coelho, A.V.P.; Kindel, A. Anuran road-kills neighboring a peri-urban reserve in the Atlantic Forest, Brazil. J. Environ. Manag. 2012, 112, 17–26.
  53. Dean, W.R.J.; Seymour, C.L.; Joseph, G.S.; Foord, S.H. A review of the impacts of roads on wildlife in semi-arid regions. Diversity 2019, 11, 81.
  54. Kreling, S.E.S.; Gaynor, K.M.; Coon, C.A.C. Roadkill distribution at the wildland-urban interface. J. Wildl. Manag. 2019, 83, 1427–1436.
  55. Meisingset, E.L.; Loe, L.E.; Brekkum, Ø.; Van Moorter, B.; Mysterud, A. Red deer habitat selection and movements in relation to roads. J. Wildl. Manag. 2013, 77, 181–191.
  56. Dussault, C.; Ouellet, J.; Laurian, C.; Courtois, R.; Poulin, M.; Breton, L. Moose movement rates along highways and crossing probability models. J. Wildl. Manag. 2007, 71, 2338–2345.
  57. Baigas, P.E.; Squires, J.R.; Olson, L.E.; Ivan, J.S.; Roberts, E.K. Using environmental features to model highway crossing behavior of Canada lynx in the Southern Rocky Mountains. Landsc. Urban Plan. 2017, 157, 200–213.
  58. Found, R.; Boyce, M.S. Predicting deer-vehicle collisions in an urban area. J. Environ. Manag. 2011, 92, 2486–2493.
  59. Bíl, M.; Andrášik, R.; Duľa, M.; Sedoník, J. On reliable identification of factors influencing wildlife-vehicle collisions along roads. J. Environ. Manag. 2019, 237, 297–304.
  60. Orłowski, G. Roadside hedgerows and trees as factors increasing road mortality of birds: Implications for management of roadside vegetation in rural landscapes. Landsc. Urban Plan. 2008, 86, 153–161.
  61. Galantinbo, A.; Santos, S.; Eufrázio, S.; Silva, C.; Carvalho, F.; Alpizar-Jara, R.; Mira, A. Effects of roads on small-mammal movements: Opportunities and risks of vegetation management on roadsides. J. Environ. Manag. 2022, 316, 115272.
  62. Helldin, J.O.; Petrovan, S.O. Effectiveness of small road tunnels and fences in reducing amphibian roadkill and barrier effects at retrofitted roads in Sweden. PeerJ 2019, 7, e7518.
  63. Clevenger, A.P.; Chruszcz, B.; Gunson, K.E. Highway mitigation fencing reduces wildlife-vehicle collisions. Wildl. Soc. B. 2001, 29, 646–653.
  64. Aresco, M.J. Mitigation measures to reduce highway mortality of turtles and other herpetofauna at a North Florida lake. J. Wildl. Manag. 2005, 69, 549–560.
  65. Bissonette, J.A.; Rosa, S. An evaluation of a mitigation strategy for deer-vehicle collisions. Wildl. Biol. 2012, 18, 414–423.
  66. Huijser, M.P.; Fairbank, E.R.; Camel-Means, W.; Graham, J.; Watson, V.; Basting, P.; Becker, D. Effectiveness of short sections of wildlife fencing and crossing structures along highways in reducing wildlife-vehicle collisions and providing safe crossing opportunities for large mammals. Biol. Conserv. 2016, 197, 61–68.
  67. Rytwinski, T.; Soanes, K.; Jaeger, J.A.G.; Fahrig, L.; Findlay, C.S.; Houlahan, J.; van der Ree, R.; van der Grift, E.A. How effective is road mitigation at reducing road-kill? A meta-analysis. PLoS ONE 2016, 11, e0166941.
  68. Colley, M.; Lougheed, S.C.; Otterbein, K.; Litzgus, J.D. Mitigation reduces road mortality of a threatened rattlesnake. Wildl. Res. 2017, 44, 48–59.
  69. van der Ree, R.; Gagnon, J.W.; Smith, D.J. Fencing: A valuable tool for reducing wildlife-vehicle collisions and funnelling fauna to crossing structures. In Handbook of Road Ecology; van der Ree, R., Smith, D.J., Grilo, C., Eds.; John Wiley & Sons: Hoboken, NJ, USA, 2015; pp. 159–171.
  70. Markle, C.E.; Gillingwater, S.D.; Levick, R.; Chow-Fraser, P. The true cost of partial fencing: Evaluating strategies to reduce reptile road mortality. Wildl. Soc. Bull. 2017, 41, 342–350.
  71. 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.
  72. Boyle, S.P.; Dillon, R.; Litzgus, J.D.; Lesbarrères, D. Desiccation of herpetofauna on roadway exclusion fencing. Can. Field Nat. 2019, 133, 43–48.
  73. Colino-Rabanal, V.J.; Lizana, M.; Peris, S.J. Factors influencing wolf Canis lupus roadkills in Northwest Spain. Eur. J. Wildl. Res. 2011, 57, 399–409.
  74. Baxter-Gilbert, J.H.; Riley, J.L.; Lesbarrères, D.; Litzgus, J.D. Mitigating reptile road mortality: Fence failures compromise ecopassage effectiveness. PLoS ONE 2015, 10, e0120537.
  75. Fensome, A.G.; Mathews, F. Roads and bats: A meta-analysis and review of the evidence on vehicle collisions and barrier effects. Mammal Rev. 2016, 46, 311–323.
  76. Gunson, K.E.; Mountrakis, G.; Quackenbush, L.J. Spatial wildlife-vehicle collision models: A review of current work and its application to transportation mitigation projects. J. Environ. Manag. 2011, 92, 1074–1082.
  77. Aquino, A.G.H.E.O.; Nkomo, S.L. Spatio-temporal patterns and consequences of road kills: A review. Animals 2021, 11, 799.
  78. Ashley, E.P.; Robinson, J.T. Road mortality of amphibians, reptiles and other wildlife on the long point causeway, Lake Erie, Ontario. Can. Field Nat. 1996, 110, 403–412.
  79. Puig, J.; Ariño, A.H.; Sanz, L. The link between roadkills distribution and the surrounding landscape in two highways in Navarre, Spain. Environ. Eng. Manag. J. 2012, 11, 1171–1178.
  80. Arévalo, J.E.; Honda, W.; Arce-Arias, A.; Häger, A. Spatiotemporal variation of roadkills show mass mortality events for amphibians in a highly trafficked road adjacent to a national park, Costa Rica. Rev. Biol. Trop. 2017, 65, 1261–1276.
  81. Ramp, D.; Caldwell, J.; Edwards, K.A.; Warton, D.; Croft, D.B. Modelling of wildlife fatality hotspots along the snowy mountain highway in New South Wales, Australia. Biol. Conserv. 2005, 126, 474–490.
  82. Farrell, M.C.; Tappe, P.A. County-level factors contributing to deer-vehicle collisions in Arkansas. J. Wildl. Manag. 2007, 71, 2727–2731.
  83. Neumann, W.; Ericsson, G.; Dettki, H.; Bunnefeld, N.; Keuler, N.S.; Helmers, D.P.; Radeloff, V.C. Difference in spatiotemporal patterns of wildlife road-crossings and wildlife vehicle collisions. Biol. Conserv. 2012, 145, 70–78.
  84. McCance, E.C.; Baydack, R.K.; Walker, D.J.; Leask, D.N. Spatial and temporal analysis of factors associated with urban deer-vehicle collisions. Hum. Wildl. Interact. 2015, 9, 119–131.
  85. Sudharsan, K.; Riley, S.J.; Campa III, H. Relative risks of deer-vehicle collisions along road types in Southeast Michigan. Hum. Dimens. Wildl. 2009, 14, 341–352.
  86. Finder, R.A.; Roseberry, J.L.; Woolf, A. Site and landscape conditions at white-tailed deer/vehicle collision locations in Illinois. Landsc. Urban Plan. 1999, 44, 77–85.
  87. Zeller, K.A.; Wattles, D.W.; Conlee, L.; Destefano, S. Response of female black bears to a high-density road network and identification of long-term road mitigation sites. Anim. Conserv. 2021, 24, 167–180.
  88. Borkovcová, M.; Mrtka, J.; Winkler, J. Factors affecting mortality of vertebrates on the roads in the Czech Republic. Transp. Res. Part D Transp. Environ. 2012, 17, 66–72.
  89. Dussault, C.; Poulin, M.; Courtois, R.; Ouellet, J. Temporal and spatial distribution of moose-vehicle accidents in the Laurentides Wildlife Reserve, Quebec, Canada. Wildl. Biol. 2006, 12, 415–425.
  90. Langen, T.A.; Machniak, A.; Crowe, E.K.; Mangan, C.; Marker, D.F.; Liddle, N.; Roden, B. Methodologies for surveying herpetofauna mortality on rural highways. J. Wildl. Manag. 2007, 71, 1361–1368.
  91. Ramp, D.; Roger, E. Frequency of animal-vehicle collisions in NSW. In Too Close for Comfort: Contentious Issues in Human-Wildlife Encounters; Lunney, D., Munn, A., Meikle, W., Eds.; Royal Zoological Society of New South Wales: Sydney, Australia, 2008; pp. 118–126.
  92. Shepard, D.B.; Dreslik, M.J.; Jellen, B.C.; Phillips, C.A. Reptile road mortality around an oasis in the Illinois corn desert with emphasis on the endangered Eastern Massasauga. Copeia 2008, 2008, 350–359.
  93. Carvalho, C.F.; Custódio, A.E.I.; Júnior, O.M. Influence of climate variables on roadkill rates of wild vertebrates in the cerrado biome, Brazil. Biosci. J. 2017, 33, 1632–1641.
  94. 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.
  95. Lee, E. The Ecological Effects of Sealed Roads in Arid Ecosystems. Ph.D. Thesis, University of New South Wales, Sydney, Australia, 2006.
  96. Wright, P.G.R.; 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.
  97. 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.
  98. Steiner, W.; Leisch, F.; Hackländer, K. A review on the temporal pattern of deer-vehicle accidents: Impact of seasonal, diurnal and lunar effects in cervids. Accid. Anal. Prev. 2014, 66, 168–181.
  99. Garrah, E.; Danby, R.K.; Eberhardt, E.; Cunnington, G.M.; Mitchell, S. Hot spots and hot times: Wildlife road mortality in a regional conservation corridor. Environ. Manag. 2015, 56, 874–889.
  100. Chen, X.; Wu, S. Examining patterns of animal-vehicle collisions in Alabama, USA. Hum. Wildl. Interact. 2014, 8, 235–244.
  101. Özcan, A.U.; Özkazanç, N.K. Identifying the hotspots of wildlife-vehicle collision on the Çankırı-kırıkkale highway during summer. Turk. J. Zool. 2017, 41, 722–730.
  102. Laliberté, J.; St-Laurent, M. In the wrong place at the wrong time: Moose and deer movement patterns influence wildlife-vehicle collision risk. Accid. Anal. Prev. 2020, 135, 105365.
  103. Kazemi, V.D.; Jafari, H.; Yavari, A. Spatio-temporal patterns of wildlife road mortality in Golestan national park-north east of Iran. Open J. Ecol. 2016, 6, 312–324.
  104. Joyce, T.L.; Mahoney, S.P. Spatial and temporal distributions of moose-vehicle collisions in Newfoundland. Wildl. Soc. Bull. 2001, 29, 281–291.
  105. Ashley, E.P.; Kosloski, A.; Petrie, S.A. Incidence of intentional vehicle-reptile collisions. Hum. Dimens. Wildl. 2007, 12, 137–143.
  106. Beckmann, C.; Shine, R. Do drivers intentionally target wildlife on roads? Austral Ecol. 2012, 37, 629–632.
  107. Kioko, J.; Kiffner, C.; Phillips, P.; Patterson-Abrolat, C.; Collinson, W.; Katers, S. Driver knowledge and attitudes on animal vehicle collisions in Northern Tanzania. Trop. Conserv. Sci. 2015, 8, 352–366.
  108. Crawford, B.A.; Andrews, K.M. Drivers’ attitudes toward wildlife-vehicle collisions with reptiles and other taxa. Anim. Conserv. 2016, 19, 444–450.
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