A simple framework for a complex problem? Predicting wildlife–vehicle collisions

Abstract

SummaryCollisions of vehicles with wildlife kill and injure animals and are also a risk to vehicle occupants, but preventing these collisions is challenging. Surveys to identify problem areas are expensive and logistically difficult. Computer modeling has identified correlates of collisions, yet these can be difficult for managers to interpret in a way that will help them reduce collision risk. We introduce a novel method to predict collision risk by modeling hazard (presence and movement of vehicles) and exposure (animal presence) across geographic space. To estimate the hazard, we predict relative traffic volume and speed along road segments across southeastern Australia using regression models based on human demographic variables. We model exposure by predicting suitable habitat for our case study species (Eastern Grey Kangaroo Macropus giganteus) based on existing fauna survey records and geographic and climatic variables. Records of reported kangaroo–vehicle collisions are used to investigate how these factors collectively contribute to collision risk. The species occurrence (exposure) model generated plausible predictions across the study area, reducing the null deviance by 30.4%. The vehicle (hazard) models explained 54.7% variance in the traffic volume data and 58.7% in the traffic speed data. Using these as predictors of collision risk explained 23.7% of the deviance in incidence of collisions. Discrimination ability of the model was good when predicting to an independent dataset. The research demonstrates that collision risks can be modeled across geographic space with a conceptual analytical framework using existing sources of data, reducing the need for expensive or time‐consuming field data collection. The framework is novel because it disentangles natural and anthropogenic effects on the likelihood of wildlife–vehicle collisions by representing hazard and exposure with separate, tunable submodels.

Comparative Analysis of the Reported Animal-Vehicle Collisions Data and Carcass Removal Data for Hotspot Identification

“…Lao et al [40] developed a diagonal inflated bivariate Poisson regression model to consider the two datasets simultaneously. To predict AVCs risk, Visintin et al [41] proposed a model that considers two types of factors: vehicles and animals.…”

Use of underpasses by animals on a fenced expressway in a suburban area in western Poland

“…Animal mortality may be attributed to various factors related to their activity in road corridors, for example, crossing the road, settling in the neighborhood of the road, or using the road as a feeding ground (Sabino-Marques and Mira 2011). Worldwide, this problem (in various degrees) ranges from urbanized and industrialized areas to natural ecosystems and affects many animal species (Hels and Buchwald 2001, Hell et al 2005, Gryz and Krauze 2008, Borkovcová et al 2012, Brzeziński et al 2012, Hothorn et al 2012, Visintin et al 2016.…”

Managing the timing and speed of vehicles reduces wildlife-transport collision risk

“…For example, identifying a collision hotspot along a 42 transportation network for a particular species can inform the most appropriate form of 43 mitigation (e.g. animal exclusion or change in vehicle speedsee Visintin et al, 2016). Collision 44 data can also be used in statistical modelling which helps to predict the probability of wildlife- (Belant, 1995;Onoyama et al, 1998;50 Wells et al, 1999) and shipping (Laist et al, 2001) operations.…”