Roads and other linear structure can break up wildlife habitats and make it harder for wildlife to move safely, often leading to dangerous collisions with vehicles. These collisions often cost money, time, and at times the lives of animals or humans. Finding ways to reduce interactions between wildlife and vehicles would be useful for both scientists and transportation planners who aim to balance connectivity conservation with road safety.
While recent U.S. policy has encouraged the construction of road mitigation structures such as overpasses and underpasses, there’s still uncertainty over whether areas that are easier for animals to travel through are more likely to have these accidents. While connectivity is often assumed to predict collision hotspots, it’s unclear whether connectivity makes some roads more dangerous than others, even when they’re close together, and how high-risk roads compare to low-risk roads across a region.
Dawson, C., Villamagna, A.M., Martin, R.A. and Moll, R.J. 2025. More connected, more collisions? Documenting nonlinear relationships between habitat connectivity and wildlife-vehicle collision hotspots. Environmental Management. DOI: 10.1007/s00267-025-02188-0.
A recent study looks at the link between habitat connectivity and wildlife-vehicle collisions (WVC) using a large dataset of over 27,000 collision records from roads across New Hampshire from 2002–2021. The authors identified 40 WVC hotspots (i.e. road segments with particularly high collision frequencies) and evaluated whether those hotspots corresponded with areas of high habitat connectivity, using a suite of generalized additive models that incorporated species-specific connectivity maps, traffic volumes, and land cover variables such as forest and agriculture.
The key finding was that more connectivity doesn’t necessarily mean more collisions. Among the four tested connectivity models, a map tailored for a barrier-sensitive species (porcupine) best predicted hotspot intensity. Interestingly, this model (and all others) showed a negative relationship between connectivity and collision intensity at hotspots.
This contrasts with earlier research suggesting more connected landscapes have higher collision rates. It’s possible that in a largely forested state like New Hampshire, even lower-connectivity zones may support sufficient movement for species, while fragmented areas may funnel wildlife through narrow corridors that intersect with roads, increasing collision risk.
Notably, a simple measure of forest cover performed nearly as well as the complex connectivity model, suggesting that in some cases, coarse habitat data can provide strong predictive power. Traffic volume emerged as a more nuanced factor: although higher average traffic volumes were found at WVC hotspots compared to other roads, collision rates at those hotspots actually peaked at moderate traffic levels, perhaps reflecting a balance between deterring wildlife crossings and not being busy enough to prevent them entirely.
The authors caution against overgeneralizing from composite connectivity maps, emphasizing that species-specific movement patterns and sensitivity to roads significantly affect outcomes. Their findings underscore the value of validating model predictions with field data and point to the importance of considering landscape context – what works in New Hampshire may not hold in more fragmented or agricultural regions.
For planners and managers, this study highlights the importance of targeted mitigation strategies. Rather than assuming all high-connectivity areas are risk zones, efforts like fencing or wildlife crossings might be best directed at locations where landscape configuration, traffic patterns, and species behaviors converge to elevate collision risk. As road networks continue to expand, integrating connectivity data into transportation planning will be key to reducing harm to both people and wildlife.
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