How animal movement influences wildlife-vehicle collision risk: a mathematical framework for range-resident species

TL;DR

This paper develops a mathematical framework linking animal movement, landscape features, and traffic to predict and analyze wildlife-vehicle collision risks, especially for range-resident mammals, aiding mitigation efforts.

Contribution

It introduces a novel theoretical model combining movement ecology and reaction-diffusion processes to quantify collision risk based on measurable animal and traffic parameters.

Findings

Derived exact expressions for collision-related survival statistics.

Identified key measurable parameters influencing collision risk.

Provided a foundation for data-driven mitigation strategies.

Abstract

Wildlife-vehicle collisions (WVC) threaten both biodiversity and human safety worldwide. Despite empirical efforts to characterize the major determinants of WVC risk and optimize mitigation strategies, we still lack a theoretical framework linking traffic, landscape, and individual movement features to collision risk. Here, we introduce such a framework by leveraging recent advances in movement ecology and reaction-diffusion stochastic processes with partially absorbing boundaries. Focusing on range-resident terrestrial mammals -- responsible for most fatal WVCs -- we model interactions with a single linear road and derive exact expressions for key survival statistics, including mean collision time and road-induced lifespan reduction. These quantities are expressed in terms of measurable parameters, such as traffic intensity or road width, and movement parameters that can be robustly estimated from relocation data, such as home-range crossing times, home-range sizes, or distance between home-range center and road. Therefore, our work provides an effective theoretical framework integrating movement and road ecology, laying the foundation for data-driven, evidence-based strategies to mitigate WVCs and promote safer, more sustainable transportation networks.