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

View Article: PubMed Central - PubMed

ABSTRACT

Collisions 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 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.

No MeSH data available.


Related in: MedlinePlus

Diagram of modeling framework. Three submodels are used to generate covariates used in the collision model per the “risk equals exposure multiplied by hazard” analytical framework.
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ece32306-fig-0001: Diagram of modeling framework. Three submodels are used to generate covariates used in the collision model per the “risk equals exposure multiplied by hazard” analytical framework.

Mentions: We apply our framework to study vehicle collisions with kangaroos in Australia using SDM to estimate kangaroo occurrence and linear regression to estimate traffic volume and speed (Fig. 1).


A simple framework for a complex problem? Predicting wildlife – vehicle collisions
Diagram of modeling framework. Three submodels are used to generate covariates used in the collision model per the “risk equals exposure multiplied by hazard” analytical framework.
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5016659&req=5

ece32306-fig-0001: Diagram of modeling framework. Three submodels are used to generate covariates used in the collision model per the “risk equals exposure multiplied by hazard” analytical framework.
Mentions: We apply our framework to study vehicle collisions with kangaroos in Australia using SDM to estimate kangaroo occurrence and linear regression to estimate traffic volume and speed (Fig. 1).

View Article: PubMed Central - PubMed

ABSTRACT

Collisions 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 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.

No MeSH data available.


Related in: MedlinePlus