Large-scale model-based assessment of deer-vehicle collision risk

Ungulates, in particular the Central European roe deer Capreolus capreolus and the North American white-tailed deer Odocoileus virginianus, are economically and ecologically important. The two species are risk factors for deer–vehicle collisions and as browsers of palatable trees have implications for forest regeneration. However, no large-scale management systems for ungulates have been implemented, mainly because of the high efforts and costs associated with attempts to estimate population sizes of free-living ungulates living in a complex landscape. Attempts to directly estimate population sizes of deer are problematic owing to poor data quality and lack of spatial representation on larger scales. We used data on 74,000 deer–vehicle collisions observed in 2006 and 2009 in Bavaria, Germany, to model the local risk of deer–vehicle collisions and to investigate the relationship between deer–vehicle collisions and both environmental conditions and browsing intensities. An innovative modelling approach for the number of deer–vehicle collisions, which allows nonlinear environment–deer relationships and assessment of spatial heterogeneity, was the basis for estimating the local risk of collisions for specific road types on the scale of Bavarian municipalities. Based on this risk model, we propose a new “deer–vehicle collision index” for deer management. We show that the risk of deer–vehicle collisions is positively correlated to browsing intensity and to harvest numbers. Overall, our results demonstrate that the number of deer–vehicle collisions can be predicted with high precision on the scale of municipalities. In the densely populated and intensively used landscapes of Central Europe and North America, a model-based risk assessment for deer–vehicle collisions provides a cost-efficient instrument for deer management on the landscape scale. The measures derived from our model provide valuable information for planning road protection and defining hunting quota. Open-source software implementing the model can be used to transfer our modelling approach to wildlife–vehicle collisions elsewhere.

Can management regulate the population size of wild reindeer (Rangifer tarandus) through harvest?

We analyzed a 51-year time series of harvest data from a small population of wild mountain reindeer ( Rangifer tarandus (L., 1758)) in southern Norway and examined the relative role of biological and management related processes as drivers of its population dynamics. The population is monitored annually to obtain information on population size and structure, and since 1980, managers have attempted to stabilize the population at about 1.1 reindeer/km2. The harvest increased at a higher rate than the population size and was thus probably sufficient to not only limit but also regulate population size. Phase plot analyses showed that the population has varied around a density attractor of about 1.0 reindeer/km2 since 1980 and is therefore close to the targeted population size of 1.1 reindeer/km2. However, the annual harvest explained only 49% of the variation in population growth rate (λ) in a linear regression model, despite relatively low variation in population productivity (proportion of calves). Between 1999 and 2006, the population in Forolhogna declined by almost 50% before recovering to its previous size. We suggest that both imprecise population estimates and high harvest effectiveness at reduced population densities contributed to this decline. As such, this study points to some of the obstacles managers are facing when trying to stabilize productive ungulate populations even when they live in closed populations and in stable, predator-free environments.

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

In addition to posing a serious risk to motorist safety, vehicle collisions with wildlife are a significant threat for many species. Previous spatial modeling has concluded that wildlife-vehicle collisions (WVCs) exhibit clustering on roads, which is attributed to specific landscape and road-related factors. We reviewed twenty-four published manuscripts that used generalized linear models to statistically determine the influence that numerous explanatory predictors have on the location of WVCs. Our motivation was to summarize empirical WVC findings to facilitate application of this knowledge to planning, and design of mitigation strategies on roads. In addition, commonalities between studies were discussed and recommendations for future model design were made. We summarized the type and measurement of each significant predictor and whether they potentially increased or decreased the occurrence of collisions with ungulates, carnivores, small-medium vertebrates, birds, and amphibians and reptiles. WVCs commonly occurred when roads bisect favorable cover, foraging, or breeding habitat for specific species or groups of species. WVCs were generally highest on road sections with high traffic volumes, or low motorist visibility, and when roads cut through drainage movement corridors, or level terrain. Ungulates, birds, small-medium vertebrates, and carnivore collision locations were associated with road-side vegetation and other features such as salt pools. In several cases, results were spurious due to confounding and interacting predictors within the same model. For example, WVCs were less likely to occur when a road bisected steep slopes; however, steep slopes may be located along specific road-types and habitat that also influence the occurrence of WVCs. In conclusion, this review showed that much of the current literature has gleaned the obvious, broad-scale relationships between WVCs and predictors from available data sets, and localized studies can provide unique and novel results. Future research requires specific modeling for each target species on a road-by-road basis, and measuring the predictive power of model results within similar landscapes. In addition, research that builds on the current literature by investigating rare anomalies and interacting variables will assist in providing sound comprehensive guidelines for wildlife mitigation planning on roads.

Segmentation of lines based on point densities--an optimisation of wildlife warning sign placement in southern Finland

Current and previous placement of wildlife warning signs by the Finnish Road Administration is based on suggestions from hunting associations and individuals. The problem within this practice is that the placement of warning signs is not a transparent process. Within this paper, we evaluate the current placement of wildlife warning signs for the Uusimaa region in southern Finland and develop a method to optimise warning sign placement using kernel density estimations, which are based on existing wildlife-vehicle accident records. The contour lines of these densities can indicate the road sections to be marked with warning signs. To apply a well-documented computational method, based on these moose and white-tailed deer accident locations, assists the Finnish Road Administration in their task to place, replace and assess wildlife-warning signs for specific road sections.