Context. Examining land cover’s influences on roadkills at single predetermined scales is more common than evaluating multiple scales, but examining land cover at the appropriate scale may be necessary for efficient design of mitigation measures, and that appropriate scale may be difficult to discern a priori. In addition, the taxonomic rank at which data is analysed may influence results and subsequent conclusions concerning mitigation.
Aims. The objective of the present study was to assess the influence of variation in spatial scales of land cover explanatory variables and taxonomic rank of response variables in models of wildlife–vehicle collisions (WVCs). Research questions include: (1) do the scales of land cover measurement that produce the highest quality models differ among species; (2) do the factors that influence roadkill events differ within species at different scales of measurement and among species overall; and (3) does the taxonomic rank at which data is analysed influence the selection of explanatory variables?
Methods. Four frequent WVC species representing diverse taxonomic classes, i.e. two mammals (Cerdocyon thous and Hydrochaeris hydrochaeris), one reptile (Caiman yacare) and one bird (Caracara plancus), were examined. WVCs were buffered, land cover classes from classified satellite imagery at three buffer radii were extracted, and logistic regression model selection was used.
Key results. The scale of land cover variables selected for the highest quality models (and the selected variables themselves) may vary among wild fauna. The same explanatory variables and formulae are not always included in the candidate models in all compared scales for a given species. Explanatory variables may differ among taxonomically similar species, e.g. mammals, and pooling species at higher taxonomic ranks can result in models that do not correspond with species-level models of all pooled species.
Conclusions. The most accurate analyses of WVCs will likely be found when species are analysed individually and multiple scales of predictor variable collection are evaluated.
Implications. Mitigating the effects of roadways on wildlife population declines for both common and rare species is resource intensive. Resources spent optimising models for spatially targeting management actions may reduce the amount of resources used and increase the effectiveness of these actions.