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.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Bartón K (2016) MuMIn: Multi-Model Inference. R package version 1.15.6. https://CRAN.R-project.org/package=MuMIn
Burnham KP, Anderson DR (2004) Model selection and multimodel inference: a practical information-theoretic approach., 2nd edn. Springer, New York, NY, USA
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 17–50
Danks ZD, Porter WF (2010) Temporal, spatial, and landscape habitat characteristics of moose-vehicle collisions in western Maine. J Wildl Manag 74:1229–1241
DeStefano S, Deblinger RD, Miller C (2005) Suburban wildlife: lessons, challenges, and opportunities. Urban Ecosyst 8:131–137
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 12:415–425
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 190:1–39
Garrett LC, Conway GA (1999) Characterstics of moose-vehicle collisions in Anchorage, Alaska, 1991–1995. J Saf Res 30:219–223
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
Groot Bruinderink GWTA, Hazebroek E (1996) Ungulate traffic collisions in Europe. Conserv Biol 10:1059–1067
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 44:268–277
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 92:1074–1082
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 32–40
Hijmans RJ (2016) raster: Geographic data analysis and modeling. R package version 2.5-8. https://CRAN.R-project.org/package=raster
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 199:142–152
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/
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 197:61–68
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 51–74
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
Jaeger JAG, Fahrig L (2004) Effects of road fencing on population persistence. Con Biol 18:1651–1657
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 70:347–357
Joyce TL, Mahoney SP (2001) Spatial and temporal distributions of moose-vehicle collisions in Newfoundland. Wildl Soc Bull 29:281–291
Lavsund S, Sandegren F (1991) Moose-vehicle relations in Sweden: a review. Alces 27:118–126
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 71:2304–2310
Lin LI (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 1:255–268
Litvaitis JA, Tash JP (2008) An approach toward understanding wildlife-vehicle collisions. Environ Manag 42:688–697
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, USA
Mannering F (2009) An empirical analysis of driver perceptions of the relationship between speed limits and safety. Transp Res Part F: Traffic Psychol Behav 12:99–106
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
MassGIS (2005) Digital Elevation Model (1:5,000). Commonwealth of Massachusetts. http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-geographic-information-massgis/datalayers/layerlist.html
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
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).
McGarigal K, Wan HY, Zeller KA, Timm BC, Cushman SA (2016) Multi-scale habitat modeling: a review and outlook. Landsc Ecol 31:1161–1175
Mountrakis G, Gunson K (2009) Multi-scale spatiotemporal analyses of moose-vehicle collisions: a case study in northern Vermont. Int J Geogr Inf Sci 23:1389–1412
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
Nielson RM, Sawyer H, McDonald TL (2013) BBMM: Brownian bridge movement model. R package version 3.0. https://CRAN.R-project.org/package=BBMM
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 145:70–78
R Core Team (2017) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
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):e0166941
Seiler A (2005) Predicting locations of moose-vehicle collisions in Sweden. J Appl Ecol 42:371–382
Silverberg JK, Perkins PJ, Robertson RA (2002) Impacts of wildlife viewing on moose use of a roadside salt lick. Alces 38:205–211
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
U. S. Census Bureau (2010) Census 2010. Resident Population Data: 530 Population Density. http://www.census.gov/2010census/data/apportionment-dens-text.php
Wattles DW (2015) The effect of thermoregulation and roads on the movements and habitat selection of moose in Massachusetts. University of Massachusetts, Amherst, USA, Dissertation
Wattles DW, DeStefano S (2011) Status and management of moose in the northeastern United States. Alces 47:53–68
Wattles DW, DeStefano S (2013) Space use and movements of moose in Massachusetts: implications for conservation of large mammals in a fragmented environment. Alces 49:65–81
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.
Conflict of Interest
The authors declare that they have no conflict of interest.
Electronic supplementary material
About this article
Cite this article
Zeller, K.A., Wattles, D.W. & DeStefano, S. Incorporating Road Crossing Data into Vehicle Collision Risk Models for Moose (Alces americanus) in Massachusetts, USA. Environmental Management 62, 518–528 (2018). https://doi.org/10.1007/s00267-018-1058-x
- Alces americanus
- Road ecology
- Wildlife–vehicle collisions