Date of Award

Fall 2025

Project Type

Thesis

Program or Major

Natural Resources

Degree Name

Master of Science

First Advisor

Remington Moll

Second Advisor

Amy Villamagna

Third Advisor

Fikirte Erda

Abstract

Chp. 1: Road networks fragment wildlife habitat and impede wildlife connectivity, which leads to elevated wildlife-vehicle collision (WVC) risk and increased danger to humans and wildlife. Habitat connectivity has been linked to WVC hotspot location and intensity, but this relationship likely depends on landscape context and road characteristics, which may be nonlinear due to varying habitat availability. Our objective was to evaluate factors affecting WVC location and intensity across New Hampshire, USA, with a focus on habitat connectivity. We assessed the relationship between WVCs and five connectivity models using generalized additive models and compared connectivity effects to road and land cover characteristics. We found that a barrier-sensitive wildlife species connectivity model was the best predictor of WVC hotspots and had a strong, negative nonlinear relationship with collision intensity. We also found that a simple forest variable performed almost as well as the complex connectivity model. WVC hotspots did not differ from adjacent roads or regional roads in terms of connectivity, except that traffic volume was higher at hotspots. Our findings suggest that the relationship between habitat connectivity and WVCs depends on broader landscape context and likely exhibits nonlinearity. Our work also demonstrates that some connectivity models are better predictors of WVCs than others, emphasizing the role of species-specific habitat connectivity assessments. These results can inform WVC mitigation planning and enhance understanding of habitat connectivity’s role in broader landscapes.

Chp. 2: Structures like drainage culverts and bridges can facilitate wildlife movement across road systems, reducing vehicle collision risk and increasing habitat connectivity. Camera traps are a useful and popular tool for monitoring the use of such crossing structures. Most commonly, cameras are used to monitor movement through a structure, but this approach inherently misses those animals in the area that are avoiding the structure and are likely crossing the road. Here, we describe a more holistic study design to better understand wildlife activity (a) in and out of the structure itself; (b) the adjacent habitat; and (c) the roadside. We employed this design at 13 wildlife-vehicle collision hotspots in New Hampshire, USA over the course of three seasons. Herein, we discuss how this design enhances ecological inference while informing applied outcomes like vehicle collision mitigation and connectivity planning. This work will encourage a more comprehensive approach to monitoring wildlife crossings.

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