Citation

Pagany R. Biol. Conserv. 2020; 251: e108758.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.biocon.2020.108758

PMID

unavailable

Abstract

Wildlife-vehicle collisions (WVCs) are caused by the close interaction of human and wildlife habitats worldwide. The large number of globally distributed accidents and the variety of environmental impacts characterize WVCs as intricate and challenging to predict. However, numerous research studies have been conducted to understand the causal relationships between drivers, animals, and the environment. In this paper, 645 publications are reviewed to provide an overview and a wide-ranging knowledge about WVC research. The study gathers the influencing factors on WVCs, systematizes the approaches for data collection, and identifies the main developments in analysis and predicting methods for WVCs. Factors such as the proximity to forest, a gentle topography with sparsely curves, street width, and seasonal differences are common denominators for WVCs - independent of the species -, while traffic volume, the distance to urban areas, or road accompanying infrastructure are not clearly assignable influencing or non-influencing factors. Different ways of data collection are observed, which range from carcass surveys by ecologists or crowdsourcing for species conservation to nearly real-time official reporting by involved parties as a basis for driving safety. Data collection and quality are discussed for their applicability, in particular, regarding the currently used analysing approaches for WVCs. Additionally, the advantages of the rarely employed machine learning approaches are discussed in terms of dynamic WVC risk prediction - including large-scale and temporally unrestricted transferability. These approaches may be helpful for prospective warning and road safety management on a global scale.

Language: en

Keywords

Dynamic warning; Environmental factor; Risk prediction; Spatial-temporal analysis; Transferability; Wildlife-vehicle collision