Advertisement

Environmental Management

, Volume 62, Issue 3, pp 518–528 | Cite as

Incorporating Road Crossing Data into Vehicle Collision Risk Models for Moose (Alces americanus) in Massachusetts, USA

  • Katherine A. Zeller
  • David W. Wattles
  • Stephen DeStefano
Article

Abstract

Wildlife–vehicle collisions are a human safety issue and may negatively impact wildlife populations. Most wildlife–vehicle collision studies predict high-risk road segments using only collision data. However, these data lack biologically relevant information such as wildlife population densities and successful road-crossing locations. We overcome this shortcoming with a new method that combines successful road crossings with vehicle collision data, to identify road segments that have both high biological relevance and high risk. We used moose (Alces americanus) road-crossing locations from 20 moose collared with Global Positioning Systems as well as moose–vehicle collision (MVC) data in the state of Massachusetts, USA, to create multi-scale resource selection functions. We predicted the probability of moose road crossings and MVCs across the road network and combined these surfaces to identify road segments that met the dual criteria of having high biological relevance and high risk for MVCs. These road segments occurred mostly on larger roadways in natural areas and were surrounded by forests, wetlands, and a heterogenous mix of land cover types. We found MVCs resulted in the mortality of 3% of the moose population in Massachusetts annually. Although there have been only three human fatalities related to MVCs in Massachusetts since 2003, the human fatality rate was one of the highest reported in the literature. The rate of MVCs relative to the size of the moose population and the risk to human safety suggest a need for road mitigation measures, such as fencing, animal detection systems, and large mammal-crossing structures on roadways in Massachusetts.

Keywords

Alces americanus Massachusetts Moose Road ecology Road-kill Wildlife–vehicle collisions 

Notes

Acknowledgements

This work was supported by the Massachusetts Division of Fisheries and Wildlife through the Federal Aid in Wildlife Restoration program (W-35-R), the Massachusetts Department of Conservation and Recreation, University of Massachusetts-Amherst, and Safari Club International. We thank J. Finn, A. Ford, T.K. Fuller, T. Lama, T. Millette, and N. Rayl for input on approach and analyses. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary material

267_2018_1058_MOESM1_ESM.docx (755 kb)
Supplementary Information

References

  1. Bartón K (2016) MuMIn: Multi-Model Inference. R package version 1.15.6. https://CRAN.R-project.org/package=MuMIn
  2. Burnham KP, Anderson DR (2004) Model selection and multimodel inference: a practical information-theoretic approach., 2nd edn. Springer, New York, NY, USACrossRefGoogle Scholar
  3. Clevenger AP, Ford AT (2010) Wildlife crossing structures, fencing, and other highway design considerations. In: Beckmann JP, Clevenger AP, Huijser MP, Hilty JA (eds) Safe passages: highways, wildlife, and habitat connectivity. Island Press, Washington, D.C., p 17–50Google Scholar
  4. Danks ZD, Porter WF (2010) Temporal, spatial, and landscape habitat characteristics of moose-vehicle collisions in western Maine. J Wildl Manag 74:1229–1241Google Scholar
  5. DeStefano S, Deblinger RD, Miller C (2005) Suburban wildlife: lessons, challenges, and opportunities. Urban Ecosyst 8:131–137CrossRefGoogle Scholar
  6. Dussault C, Poulin M, Courtois R, Ouellet J-P (2006) Temporal and spatial distribution of moose-vehcile accidents in the Laurentides Wildlife Reserve, Quebec, Canada. Wildl Biol 12:415–425CrossRefGoogle Scholar
  7. Fedy BC, Doherty KE, O’Donnell M, Beck JL, Bedrosian B, Gummer D, Holloran MJ, Johnson GD, Kaczor NW, Kirol CP, Mandich CA, Marshall D, McKee G, Olson C, Pratt AC, Swanson CC, Walker BL (2014) Habitat prioritization across large landscapes, multiple seasons, and novel areas: an example using greater sage-grous in Wyoming. Wildl Monogr 190:1–39CrossRefGoogle Scholar
  8. Garrett LC, Conway GA (1999) Characterstics of moose-vehicle collisions in Anchorage, Alaska, 1991–1995. J Saf Res 30:219–223CrossRefGoogle Scholar
  9. Gilleland E (2013) Two-dimensional kernel smoothing: using the R package smoothie. NCAR Technical Note, TN-502+STR, p 17 http://opensky.library.ucar.edu/collections/TECH-NOTE-000-000-000-869
  10. Groot Bruinderink GWTA, Hazebroek E (1996) Ungulate traffic collisions in Europe. Conserv Biol 10:1059–1067CrossRefGoogle Scholar
  11. Gunson KE, Clevenger AP, Ford AT, Bissonette JT, Hardy A (2009) A comparison of data sets varying in spatial accuracy used to predict the occurrence of wildlife-vehicle collisions. Environ Manag 44:268–277CrossRefGoogle Scholar
  12. Gunson KE, Mountrakis G, Quackenbush LJ (2011) Spatial wildlife-vehicle collision models: a review of current work and its application to transportation mitigation projects. J Environ Manag 92:1074–1082CrossRefGoogle Scholar
  13. Gunther KA, Biel MJ, Robison HL Factors influencing the frequency of road-killed wildlife in Yellowstone National Park. In: Evink GL Garrett P, Zeigler D, Berry J (eds) Proceedings of the International Conference on Wildlife Ecology and Transportation. FL-ER-69-98. Department of Transportation, Tallahassee, pp 32–40Google Scholar
  14. Hijmans RJ (2016) raster: Geographic data analysis and modeling. R package version 2.5-8. https://CRAN.R-project.org/package=raster
  15. Hirzel AH, Le Lay G, Helfer V, Randin C, Guisan A (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecol Modell 199:142–152CrossRefGoogle Scholar
  16. Huijser MP, Duffield JW, Clevenger AP, Ament RJ, McGowen PT (2009) Cost-benefit analyses of mitigation measures aimed at reducing collisions with large ungulates in North America; a decision support tool. Ecol Soc 14:15, http://www.ecologyandsociety.org/vol14/issue2/art15/
  17. Huijser MP, Fairbank ER, Camel-Means W, Graham J, Watson V, Basting P, Becker D (2016) 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 197:61–68CrossRefGoogle Scholar
  18. Huijser MP, McGowen PT (2010) Reducing wildlife-vehicle collisions. In: Beckmann JP, Clevenger AP, Huijser MP, Hilty JA (eds) Safe passages: highways, wildlife, and habitat connectivity. Island Press, Washington, D.C., p 51–74Google Scholar
  19. Huijser MP, McGowen P, Clevenger AP, Ament R (2008) Wildlife-vehicle collision reduction study: best practices manual. Western Transportation Institute, Montana State University, Bozeman, USA, https://westerntransportationinstitute.org/wp-content/uploads/2016/08/4W1096_Best_Practices_Manual.pdf
  20. Jaeger JAG, Fahrig L (2004) Effects of road fencing on population persistence. Con Biol 18:1651–1657CrossRefGoogle Scholar
  21. Johnson CJ, Nielsen SE, Merrill EH, McDonald TL, Boyce MS (2006) Resource selection functions based on use-availability data: theoretical motivations and evaluations methods. J Wildl Manag 70:347–357CrossRefGoogle Scholar
  22. Joyce TL, Mahoney SP (2001) Spatial and temporal distributions of moose-vehicle collisions in Newfoundland. Wildl Soc Bull 29:281–291Google Scholar
  23. Lavsund S, Sandegren F (1991) Moose-vehicle relations in Sweden: a review. Alces 27:118–126Google Scholar
  24. Leblond M, Dussault C, Ouellet J-P, Poulin M, Courtois R, Fortin J (2007) Management of roadside salt pools to reduce moose-vehicle collisions. J Wildl Manag 71:2304–2310CrossRefGoogle Scholar
  25. Lin LI (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 1:255–268CrossRefGoogle Scholar
  26. Litvaitis JA, Tash JP (2008) An approach toward understanding wildlife-vehicle collisions. Environ Manag 42:688–697CrossRefGoogle Scholar
  27. Manly BFJ, McDonald LL, Thomas DL, McDonald TL, Erickson WP (2002) Resource selection by animals: statistical analysis and design for field studies, 2nd edn. Kluwer, Boston, Massachusetts, USAGoogle Scholar
  28. Mannering F (2009) An empirical analysis of driver perceptions of the relationship between speed limits and safety. Transp Res Part F: Traffic Psychol Behav 12:99–106CrossRefGoogle Scholar
  29. Massachusetts Department of Transportation (2013) MassDOT roads 1:5,000 road and rail centerlines. http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-geographic-information-massgis/datalayers/layerlist.html
  30. McClure M, Ament R (2014) Where people and wildlife intersect: prioritizing mitigation of road impacts on wildlife connectivity. Center for Large Landscape Conservation report. Pp. 56. http://largelandscapes.org/media/publications/Where-People--Wildlife-Intersect-Prioritizing-Mitigation.pdf
  31. McGarigal K, Compton BW, Jackson SD, Plunkett E, Rolith K, Portante T, Ene E (2015) Conservation Assessment and Prioritization System (CAPS) Statewide Massachusetts Assessment. Landscape Ecology Program Department of Environmental Conservation University of Massachusetts, Amherst. http://www.umasscaps.org (accessed 5 July 2017).
  32. McGarigal K, Wan HY, Zeller KA, Timm BC, Cushman SA (2016) Multi-scale habitat modeling: a review and outlook. Landsc Ecol 31:1161–1175CrossRefGoogle Scholar
  33. Mountrakis G, Gunson K (2009) Multi-scale spatiotemporal analyses of moose-vehicle collisions: a case study in northern Vermont. Int J Geogr Inf Sci 23:1389–1412CrossRefGoogle Scholar
  34. National Highway Traffic Safety Administration (2016) Traffic safety facts: Driver electronic device use in 2015. U.S. Department of Transportation Research Note. https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/driver_electronic_device_use_in_2015_0.pdf
  35. Nielson RM, Sawyer H, McDonald TL (2013) BBMM: Brownian bridge movement model. R package version 3.0. https://CRAN.R-project.org/package=BBMM
  36. Neumann W, Ericsson G, Dettki H, Bunnefeld N, Keuler NS, Helmers DP, Radeloff VC (2012) Difference in spatiotemporal patterns of wildlife road-crossings and wildlife-vehicle collisions. Biol Conserv 145:70–78CrossRefGoogle Scholar
  37. R Core Team (2017) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
  38. Rytwinski T, Soanes K, Jaeger JAG, Fahrig L, Findlay CS, Houlahan J, van der Ree R, van der Grift EA (2016) How effective is road mitigation at reducing road-kill? A meta-analysis. PLoS ONE 11(11):e0166941CrossRefGoogle Scholar
  39. Seiler A (2005) Predicting locations of moose-vehicle collisions in Sweden. J Appl Ecol 42:371–382CrossRefGoogle Scholar
  40. Silverberg JK, Perkins PJ, Robertson RA (2002) Impacts of wildlife viewing on moose use of a roadside salt lick. Alces 38:205–211Google Scholar
  41. Teixeira FZ, Kindel A, Hartz SM, Mitchell S, Fahrig L (2017) When road-kill hotspots do not indicate the best sites for road-kill mitigation. J Appl Ecol  https://doi.org/10.1111/1365-2664.12870
  42. U. S. Census Bureau (2010) Census 2010. Resident Population Data: 530 Population Density. http://www.census.gov/2010census/data/apportionment-dens-text.php
  43. Wattles DW (2015) The effect of thermoregulation and roads on the movements and habitat selection of moose in Massachusetts. University of Massachusetts, Amherst, USA, DissertationGoogle Scholar
  44. Wattles DW, DeStefano S (2011) Status and management of moose in the northeastern United States. Alces 47:53–68Google Scholar
  45. Wattles DW, DeStefano S (2013) Space use and movements of moose in Massachusetts: implications for conservation of large mammals in a fragmented environment. Alces 49:65–81Google Scholar

Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2018

Authors and Affiliations

  1. 1.Massachusetts Cooperative Fish and Wildlife Research UnitUniversity of MassachusettsAmherstUSA
  2. 2.U.S. Geological Survey, Massachusetts Cooperative Fish and Wildlife Research UnitUniversity of MassachusettsAmherstUSA

Personalised recommendations