Incorporating habitat use in models of fauna fatalities on roads

Abstract

Aim To highlight the benefit of using habitat use to improve the accuracy of predictive road fatality models.Location The Snowy Mountains Highway in southern New South Wales, Australia.Methods A binary logistic regression model was constructed using wombat fatality presences and randomly generated absences. Species‐specific habitat variables were included as predictors in the model selection process as well as two spatially explicit measures of wombat habitat use. Generalized additive models (GAMs) were constructed for each possible combination of predictors in R. The final model was selected by comparing all models subsets for the eight predictors and employing the one standard error rule to select the best model set.Results The final predictive model had high discriminatory power and incorporated both measures of species habitat use, greatly exceeding the variation explained by a previously published model for the same species and road.Main Conclusions Our findings highlight the importance of incorporating variables that describe habitat use by fauna for predictive modelling of animal‐vehicle crashes. Reliance upon models that ignore landscape patterns are limited in their capacity to identify hotspots and inform managers of locations to engage in mitigation.

Relative Risk and Variables Associated with Bear and Ungulate Mortalities Along a Railroad in the Canadian Rocky Mountains

“…Landscape variables derived from land cover data have been found to be the best predictors of road-kill rates (Bashore et al 1985;Finder et al 1999;Roger and Ramp 2009). We suggest that estimates of relative abundance be used to assess the spatial pattern of road-kills and train strikes.…”

Spatial wildlife-vehicle collision models: A review of current work and its application to transportation mitigation projects

“…Intuitively, wildlife-motorist conflicts can be alleviated by avoiding natural landscape features and species-specific nesting, cover, or foraging habitat (e.g., Nielsen et al, 2003;Orlowski, 2005;Roger and Ramp, 2009) during route planning. For example, findings show a mixture of forest-open habitat surrounding a road will increase the occurrence of collisions with ungulates (e.g., Finder et al, 1999;Malo et al, 2004;Gunson et al, 2009) and the presence of wetlands will increase the risk of vehicle collisions with amphibians and reptiles (e.g., Glista et al, 2007;Langen et al, 2009).…”

Priority ranking of road sites for mitigating wildlife roadkill

“…Unlike studies that aim to establish predictive models of roadkill (Malo et al 2004, Gomes et al 2009, Roger & Ramp 2009), the RI intends to rank the kilometers and identify those in which the installation of mitigation devices is a priority. The proposal for the RI presented here was developed in one case study, based on sections of one kilometer.…”

“…Brazilian studies seek to establish mitigation strategies for a large number of species (Coelho et al 2008, GumierCosta & Sperber 2009, Turci & Bernarde 2009), normally involving different classes. Outside Brazil, the majority of publications focus on a specific species (Finder et al 1999, Gomes et al 2009, Roger & Ramp 2009), order (Aresco 2005), or class (Hels & Buchwald 2001). Possibly this difference is related to the existing species richness, or to economic issues as well.…”

“…The identification of hotspots has involved such varied areas as the criminal (Glässer & Vajihollahi 2008) or health (d'Aignaux et al 2001, used in the definition of public policies, identification of priority areas for conservation (Myers et al 2000), and in critical roadkill areas (Ramp et al 2005, Gomes et al 2009, Roger & Ramp 2009). Brazil lacks studies on wildlife roadkill that are aimed at the implementation and evaluation of mitigation measures, and most studies have focused on listing the species that are run over (Bager et al 2007).…”