Wild animals and vehicles – analysis of development of a conflict: case of sverdlovsk region

Мұқаба

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Тек жазылушылар үшін

Аннотация

Abstract –The dynamics of the number of road accidents with wild animals in the Sverdlovsk region for the period from 2012 to 2022 was analyzed. The species composition of the animals is sharply shifted towards pair-horned ungulates. The increase in the number of collisions with roe deer and moose is faster than the increase in the number of species by an average of 3.1 times. The seasonal peak of incidents occurs in May-July for moose and Siberian roe deer and in October for wild boar. A strong correlation was found between the number of road incidents, roe deer and moose numbers and vehicle density on roads. The rate of increase in animal populations is 31 and 33% (for roe deer and moose, respectively) of the rate of increase in the number of accidents, while the rate of increase in vehicle density on roads is 7.5–9.9%. It is suggested that the impact of animal population growth on the change in the number of accidents is higher than the impact of the change in traffic intensity.

Толық мәтін

Рұқсат жабық

Авторлар туралы

N. Korytin

Institute of Plant and Animal Ecology, Ural Branch, Russian Academy of Sciences

Хат алмасуға жауапты Автор.
Email: nsk@ipae.uran.ru
Ресей, Ekaterinburg

N. Markov

Institute of Plant and Animal Ecology, Ural Branch, Russian Academy of Sciences

Email: nsk@ipae.uran.ru
Ресей, Ekaterinburg

A. Kuznetsov

Department for the Protection, Control and Regulation of the Use of Wildlife of the Sverdlovsk Region

Email: nsk@ipae.uran.ru
Ресей, Ekaterinburg

I. Bergman

Institute of Plant and Animal Ecology, Ural Branch, Russian Academy of Sciences

Email: nsk@ipae.uran.ru
Ресей, Ekaterinburg

Әдебиет тізімі

  1. Наумов Н.П. Экология животных. М.: Высшая школа, 1963. 618 с.
  2. Алтухов Ю.П., Салменкова Е.А., Курбатова О.Л. и др. Динамика популяционных генофондов при антропогенных воздействиях / Под ред. Алтухова Ю.П. М.: Наука, 2004. 619 с. http://www.mce.biophys.msu.ru/eng/books/publisher32000/book38256/
  3. Хански И. Ускользающий мир: Экологические последствия утраты местообитаний. М.: Тов-во научн. изд. КМК, 2010. 340 с.
  4. Cardinale B.J., Duffy J.E., Gonzalez A. et al. Biodiversity loss and its impact // Nature. 2012. V. 486. P. 59–67. https://doi.org/10.1038/nature11148
  5. 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. V. 92. № 4. P. 1074–1082. https://doi.org/10.1016/j.jenvman.2010.11.027
  6. Jägerbrand A.K., Antonson H. Driving behaviour responses to a moose encounter, automatic speedcamera, wildlife warning sign and radio message determinedin a factorial simulator study // Accid. Anal. Preven. 2016. V. 86. P. 229–238. https://doi.org/10.1016/j.aap.2015.11.004
  7. Niemi M., Rolandsen C.M, Neumann W. et al. Temporal patterns of moose-vehicle collisions with and without personal injuries // Accid. Anal. Preven. 2017. V. 98. P. 167–173. https://doi.org/10.1016/j.aap.2016.09.024
  8. Santos R.A., Mota-Ferreira M., Aguiar L.M. et al. Predicting wildlife road-crossing probability from roadkill data using occupancy-detection models // Sci. Total Environ. 2018. V. 642. P. 629–637. https://doi.org/10.1016/j.scitotenv.2018.06.107
  9. Bartonička T., Andrášik R., Dul’a M. et al. Identification of local factors causing clustering of animal-vehicle collisions // J. Wildl. Manag. 2018. V. 82. № 5. P. 940–947. https://doi.org/10.1002/jwmg.21467
  10. Bíl M., Andrášik R., Duľa M., Sedoník J. On reliable identification of factors influencing wildlife-vehicle collisions along roads // J. Environ. Manag. 2019. V. 237. P. 297–304. https://doi.org/10.1016/j.jenvman.2019.02.076
  11. Bíl M., Kubeček J., Andrášik R. Ungulate-vehicle collision risk and traffic volume on roads // Eur. J. Wildl. Res. 2020. V. 66. № 59. P. 1‒10. https://doi.org/10.1007/s10344-020-01397-8
  12. Pagany R. Wildlife-vehicle collisions ‒ Influencing factors, data collection and research methods // Biol. Conserv. 2020. V. 251. P. 1‒26. https://doi.org/10.1016/j.biocon.2020.108758
  13. Fernández-López J., Blanco-Aguiar J.A., Vicente J. et al. Can we model distribution of population abundance from wildlife-vehicles collision data? // Ecography. 2022. V. 2022. № 5. P. 1‒11. https://doi.org/10.1111/ecog.06113
  14. Gurumurthy K.M., Bansal P., Kockelman K.M. et al. Modelling animal-vehicle collision counts across large networks using a bayesian hierarchical model with time-varying parameters // Anal. Methods Accid. Res. 2022. V. 36. P. 1–19. https://doi.org/10.1016/j.amar.2022.100231
  15. Krukowicz T., Firlag K., Chrobot P. Spatiotemporal analysis of road crashes with animals in Poland // Sustainability. 2022. V. 14. № 3. P. 1–31. https://doi.org/10.3390/su14031253
  16. Perez-Guerra J., Gonzalez-Velez J., Murillo-Escobar J. et al. Prediction of areas with high risk of roadkill wildlife applying maximum entropy approach and environmental features: East Antioquia, Colombia // Landscape and Ecological Engineering. 2024. V. 20. P. 75–88. https://doi.org/10.1007/s11355-023-00581-7
  17. Laube P., Ratnaweera N., Wróbel A. et al. Analysing and predicting wildlife-vehicle collision hotspots for the Swiss Road Network // Landscape Ecology. 2023. V. 38. P. 1765–1783. https://doi.org/10.1007/s10980-023-01655-5
  18. Ирхина Е.С. Совершенствование системы природоохранных мероприятий на путях миграции лосей (на примере Ленинградской области): Вып. квалификац. работа магистра. СПб: СПбГУ, 2017. 50 с. https://core.ac.uk/download/pdf/217180794.pdf
  19. Селезнева А.В., Карев С.В. Решение проблемы дорожно-транспортных происшествий с участием диких животных // Научное обозрение. Педагогические науки. 2019. № 2‒3. С. 78‒82. https://www.elibrary.ru/item.asp?id=38214494
  20. Бобышев М.В., Чураев А.Ю., Спасик С.Е. Анализ причин дорожно-транспортных происшествий с участием лосей на территории Рузского района Московской области в 2015‒2020 гг. // Вестник РГАЗУ. 2020. № 34 (39). С. 20–27. https://www.elibrary.ru/item.asp?id=44244158
  21. Просеков А.Ю. Анализ дорожно-транспортных происшествий с участием косули и лося в Кузбассе // Охрана и рациональное использование животных и растительных ресурсов: Мат-лы междун. научно-практич. конф., приуроченной к 120-летию со дня рождения проф. В.Н. Скалона, в рамках ХII междун. научно-практич. конференции “Климат, экология, сельское хозяйство Евразии”. Ч. I. Молодежный, 2023. С. 195‒200. https://www.elibrary.ru/item.asp?id=54065075
  22. Селезнева А.В., Григорьева Т.Ю. Оценка эффективности мероприятий по снижению количества ДТП с участием диких животных // European J. of Natural History. 2020. № 2. P. 90–95. https://www.elibrary.ru/item.asp?id=42564365
  23. Конева О.В., Демидов Д.В. Состояние аварийности, связанной с наездом автотранспортных средств на диких животных по территории Свердловской области // Научное творчество молодежи – лесному комплексу России: Мат-лы XIX Всероссийской (национальной) научно-технич. конф. студентов и аспирантов. Ответственный за выпуск Малютина Л. В. Екатеринбург, 2023. С. 732–736.
  24. Ермолова М.В., Трошина Е.И. Количество дорожно-транспортных происшествий с пострадавшими по субъектам Российской Федерации (Электронный ресурс). https://rosstat.gov.ru/storage/mediabank/postrad.xls (дата обращения 30.08.2023).
  25. Neumann W., Ericsson G., Dettki H. et al. Difference in spatiotemporal patterns of wildlife road-crossings and wildlife-vehicle collisions // Biol. Conserv. 2012. V. 145. № 1. P. 70‒78. https://doi.org/10.1016/j.biocon.2011.10.011
  26. Млекопитающие Свердловской области: Cправочник-определитель / В.Н. Большаков и др. Екатеринбург: Екатеринбург, 2000. 240 с.
  27. Волкова И.Н. Проблемы и перспективы территориальной организации и территориального планирования региональной транспортной системы (на примере Свердловской области) // Вестник Удмуртского ун-та. Сер.: Биология и науки о Земле. 2022. Т. 32. Вып. 2. С. 192‒203.
  28. Трошина Е.И. Протяженность и характеристики автомобильных дорог общего пользования по субъектам Российской Федерации. https://rosstat.gov.ru›storage/mediabank/prot_avto_dor (дата обращения 30.08.2023).
  29. Ермолова М.В., Жукова А.В. Наличие транспортных средств по Российской Федерации. https://rosstat.gov.ru/statistics/transport (дата обращения 30.08.2023).
  30. Витрина статистических данных: численность постоянного населения в среднем за год (Электронный ресурс). https://showdata.gks.ru/report/278930/
  31. Коли Г. Анализ популяций позвоночных. М.: Мир, 1979. 362 с.
  32. R Core Team. R: a language and environment for statistical computing. https://www.R-project.org/. (дата обращения 01.09.2023).
  33. Signorell A., Aho K., Alfons A. et al. DescTools: Tools for descriptive statistics. R package version 0.99.23. https://andrisignorell.github.io/DescTools/ (дата обращения 01.09.2023).
  34. Bruinderink Groot G.W.T.A., Hazebroek E. Ungulate traffic collisions in Europe // Conservation Biology. 1996. V. 10. № 4. P. 1059–1067. https://doi.org/10.1046/j.1523-1739.1996.10041059.x
  35. Seiler A. Trends and spatial patterns in ungulate-vehicle collisions in Sweden // Wildl. Biol. 2004. V. 10. № 4. P. 301‒313. https://doi.org/10.2981/wlb.2004.036
  36. Cserkecz T., Farkas J. Annual trends in the number of wildlife-vehicle collisions on the main linear transport corridors (highway and railway) of Hungary // North-West. J. Zool. 2015. V.11. № 1. P. 41–50. https://biozoojournals.ro/nwjz/content/v11n1/nwjz_141707_Cserkesz.pdf
  37. Raymond S., Spencer M., Chadwick E.A. et al. The impact of the COVID-19 lockdowns on wildlife-vehicle collisions in the UK // J. Anim. Ecol. 2023. V. 92. P. 1244–1255. https://doi.org/10.1111/1365-2656.13913
  38. Grilo C., Koroleva E., Andrášik R. et al. Roadkill risk and population vulnerability in European birds and mammals // Front. Ecol. Environ. 2020. V. 18. № 6. P. 323–328. https://doi.org/10.1002/fee.2216
  39. Raymond S., Schwartz A.L.W., Thomas R.J. et al. Temporal patterns of wildlife roadkill in the UK // PLoS ONE. 2021. V. 16. № 10. Art. e0258083. https://doi.org/10.1371/journal.pone.0258083
  40. Vance J.A., Smith W.H., Smith G.L. Species composition and temporal patterns of wildlife-vehicle collisions in southwest Virginia, USA // Human-Wildlife Interactions. 2018. V. 12. № 3. https://doi.org/10.26077/6a2c-cj16
  41. Lavsund S., Sandegren F. Moose-vehicle relations in Sweden: a review // Alces: A Journal Devoted to the Biology and Management of Moose. 1991. V. 27. P. 118‒126. https://www.alcesjournal.org/index.php/alces/article/view/1109
  42. Madsen A.B., Strandgaard H., Prang A. Factors causing traffic killings of roe deer Capreolus capreolus in Denmark // Wildl. Biol. 2002. V. 8. № 1. P. 55–61.https://doi.org/10.2981/wlb.2002.008
  43. Putzu N., Bonetto D., Civallero V. et al. Temporal patterns of ungulate-vehicle collisions in a subalpine Italian region // Ital. J. Zool. 2014. V. 81. № 3. P. 463–470.https://doi.org/10.1080/11250003.2014.945974
  44. Grilo C., Bissonette J.A., Santos-Reis M. Spatial-temporal patterns in Mediterranean carnivore road casualties: Consequences for mitigation // Biol. Conser. 2009. V. 142. № 2. P. 301‒313. https://doi.org/10.1016/j.biocon.2008.10.026
  45. Neumann W., Ericsson G., Dettki H. et al. Difference in spatiotemporal patterns of wildlife road-crossings and wildlife-vehicle collisions // Biol. Conserv. 2012. V. 145. № 1. P. 70‒78. https://doi.org/10.1016/j.biocon.2011.10.011
  46. Mastro L.L., Conover M.R., Frey S.N. Factors influencing a motorist’s ability to detect deer at night // Landsc. Urban Plann. 2010. V. 94. № 3‒4. P. 250‒254. https://doi.org/10.1016/j.landurbplan.2009.10.010
  47. Rodgers A.R., Robins P.J. Moose detection distances on highways at night // Alces: A Journal Devoted to the Biology and Management of Moose. 2006. V. 42. P. 75‒87. https://alcesjournal.org/index.php/alces/article/view/389
  48. Vrkljan J., Hozjan D., Barić D. et al. Temporal patterns of vehicle collisions with roe deer and wild boar in the Dinaric Area // Croat. J. For. Eng. 2020. V. 41. № 2. P. 347–358. https://hrcak.srce.hr/240283
  49. Psiropoulos J.L., Howe E., Mayer J.J. et. al. Characterization of recent wild pig-vehicle collisions in Georgia, USA // Mamm. Res. 2024. V. 69. P. 131–144. https://doi.org/10.1007/s13364-023-00724-z
  50. Печатнова Е.В., Подрезова А.Ю. Выявление основных особенностей аварийности, связанной с наездами на животных // Вестник КемРИПК. 2019. № 1. С. 103–107. https://www.elibrary.ru/download/elibrary_39158302 _44134194.pdf
  51. Shepherd B.J., Houck J., Lyon C. On the road again: A study valuing wildlife crossings for wetland mitigation on State Road 40 in Volusia County, Florida // Ecosphere. 2023. V. 14. № 6. Art. e4566. https://doi.org/10.1002/ecs2.4566
  52. Clevenger A.P., Waltho N. Factors influencing the effectiveness of wildlifeunderpasses in Banff National Park, Alberta, Canada // Conserv. Biol. 2000. V. 14. № 1. P. 47‒56. https://doi.org/10.1046/j.1523-1739.2000.00099-085.x
  53. Foster M.L., Humphrey S.R. Use of highway underpasses by Florida panthers and other wildlife // Wildl. Soc. Bull. 1995. V. 23. № 1. P. 95‒100. https://www.jstor.org/stable/3783202
  54. Dodd N.L., Gagnon J.W., Manzo A.L. et. al. Video surveillance to assess highway underpass use by elk in Arizona // J. Wildl. Manag. 2007. V. 71. № 2. P. 637‒645. https://doi.org/10.2193/2006-340
  55. Mata C., Hervas I., Herranz J. et al. Are motorway wildlife passages worth building? Vertebrate use of road-crossing structures on a Spanish motorway // J. Environ. Manag. 2008. V. 88. № 3. P. 407‒415. https://doi.org/10.1016/j.jenvman.2007.03.014
  56. Dodd C.K., Barichivich W.J., Smith L.L. Effectiveness of a barrier wall and culverts in reducing wildlife mortality on a heavily traveled highway in Florida // Biol Conserv. 2004. V. 118. № 5. P. 619‒631. https://doi.org/10.1016/j.biocon.2003.10.011
  57. Romin L.A., Bissonette J.A. Deer-vehicle collisions: status of state monitoring activities and mitigation efforts // Wildl. Soc. Bull. 1996. V. 24. № 2. P. 276‒283. https://www.jstor.org/stable/3783118
  58. D’Angelo G.J., D’Angelo J.G., Gallagher G.R. et al. Evaluation of wildlife warning reflectors for altering white-tailed deerbehavior along roadways // Wildl. Soc. Bull. 2006. V. 34. № 4. P. 1175‒1183. https://doi.org/10.2193/0091-7648(2006)34[1175:EOWWRF]2.0.CO;2
  59. Huijser M.P., McGowen P., Fuller J. et al. Wildlife-vehicle Collision Reduction Study. Report to U.S.Congress. U.S. Department of Transportation. Federal Highway Administration. 2007. Washington, D.C. p. 232. https://wafwa.org/wp-content/uploads/2021/04/2007-Report-to-Congress.pdf

Қосымша файлдар

Қосымша файлдар
Әрекет
1. JATS XML
Жүктеу
2. Fig. 1. Dynamics of the total number of road accidents involving wild animals in the Sverdlovsk region for the period 2012‒2022.

Жүктеу (76KB)
Метадеректер
3. Fig. 2. Dynamics of road accidents involving wild ungulate mammals in the Sverdlovsk region: a – roe deer; b – elk; c – wild boar.

Жүктеу (144KB)
Метадеректер
4. Fig. 3. Seasonal dynamics of the absolute number of accidents with ungulate mammals: a – roe deer; b – elk; c – wild boar. The line, rectangle and whiskers indicate the median, interquartile range and range of values ​​(minimum – maximum), respectively.

Жүктеу (145KB)
Метадеректер
5. Fig. 4. Change in the number of ungulate mammals (1 ‒ elk, 2 ‒ roe deer) in the Sverdlovsk region in 2012‒2022.

Жүктеу (62KB)
Метадеректер
6. Fig. 5. Change in the number of cars (1) and the length of roads (2) in the Sverdlovsk region in 2012‒2022.

Жүктеу (87KB)
Метадеректер
7. Fig. 6. Dependence of the number of animals injured in road accidents (in % of the total number) on the number of cars (Ai): a – roe deer (y = ‒5.22e-1 + 4.47e-7*x, R2 = 0.55, p < 0.001); b – elk (y = ‒2.19e-1 + 2.32e-7*x, R2 = 0.86, p < 0.001). The original values, regression line and 95% confidence interval are shown.

Жүктеу (98KB)
Метадеректер
8. Fig. 7. Dependence of the number of animals injured in road accidents (in % of the total number) on the number of cars per 1 km of road (Ai /Di): a ‒ roe deer (y = ‒6.37e-1+1.57e-2*x, R2 = 0.36, p = 0.03); b ‒ elk (y = ‒1.66e-1+6.03e-3*x, R2 = 0.27, p = 0.058). The original values, regression line and 95% confidence interval are shown.

Жүктеу (99KB)
Метадеректер

© Russian Academy of Sciences, 2024