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

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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.

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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.

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Authors and Affiliations

  1. Massachusetts Cooperative Fish and Wildlife Research Unit, University of Massachusetts, Amherst, MA, 01003, USA

    Katherine A. Zeller & David W. Wattles

  2. U.S. Geological Survey, Massachusetts Cooperative Fish and Wildlife Research Unit, University of Massachusetts, Amherst, MA, 01003, USA

    Stephen DeStefano

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  1. Katherine A. Zeller
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  2. David W. Wattles
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  3. Stephen DeStefano
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Correspondence to Katherine A. Zeller.

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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

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