These last decades, simultaneous increase in infrastructure networks and ungulates populations (Burbaitė and Csányi 2010; Milner et al. 2006; Saez-Royuela and Telleria 1986) has lead to an increase in the number of wildlife vehicle collisions (Groot Bruinderink and Hazebroek 1996; Romin and Bissonette 1996; Seiler 2004). WVC have important social, economic and ecological consequences. Socially, WVC threaten traffic and human safety and cause injuries, trauma and in some cases death of car drivers or passengers (Williams and Wells 2005). Although WVC involve any size of species, car damage, injuries or fatalities are mostly caused by collision with larger species (> 30kg) (Barthelmess and Brooks 2010; Ford and Fahrig 2007). Economically, these accidents have also high impact through vehicle damage. Hence, in France this cost has been evaluated to 200 million euros for the year 2003, five times more than the cost of damage to agriculture by wildlife for the same year (Vignon and Barbarreau 2008). Ecologically, WVC induce population reduction (Lodé 2000) and can impact differently animal populations (Bissonette and Adair 2008). It can affect weakly animal population when accounting for a small part only of the population mortality (Groot Bruinderink and Hazebroek 1996), moderately when reaching mortality level equal to hunting activities (Forman and Alexander 1998; Gosselink et al. 2007) or greatly in some cases when population viability is threaten (Huijser and Bergers 2000; Kramer-Schadt et al. 2004). Roadsalso act as barriers to animal movement within the landscape causing local population disappearance (Shepard et al. 2008) and isolating animal population on several unconnected subpopulations under higher risk of extinction (Lodé 2000). Transport infrastructures also induce habitat modifications and landscape fragmentation (Forman and Alexander 1998).

To develop effective mitigation measures, knowledge about location and time of road casualties involving wildlife is required. It is important that every country undertakes road ecology researches because any situation is specific and can bring new relevant information on patterns of WVC. Moreover standardised data collection at national scale could help facilitating meta-analysis (Knapp 2005), understanding effect of density and configuration of transport infrastructure on ecosystems functioning at broader scale (van der Ree et al. 2011) and facing the problem of time and space extrapolation (Roedenbeck et al. 2007). Road ecology requires thus now more collaboration between countries to develop adapted mitigation strategies and improve road planning in areas where transport network is still under development.

Literature about factors explaining wildlife-vehicle collisions is abundant, demonstrating the importance of this topic. During these last years many studies have brought up results on WVC for numerous countries, e.g. Spain (Colino–Rabanal et al. 2012; Diaz-Varela et al. 2011; Lagos et al. 2012; Rodríguez-Morales et al. 2013), Hungary (Cserkész et al. 2013; Markolt et al. 2012), Sweden (Neumann et al. 2012), Poland (Tajchman et al. 2010), USA (Danks and Porter 2010), Ireland (Haigh 2012). Road characteristics, traffic volume, drivers visibility and intersection of roads are important causes of WVC (Colino–Rabanal et al. 2012; Gunson et al. 2011; Madsen et al. 2002; Seiler 2004). Temporal trends in number WVC are known to match animal behavior and biology, with WVC occurring mainly during breeding and dispersal period at a seasonal scale, or to daily foraging and resting requirement of animal (Groot Bruinderink and Hazebroek 1996; Haigh 2012; Diaz-Varela et al. 2011).

However, in Wallonia, southern part of Belgium (Figure 1), no published data on accidents involving large species has been published so far. Contrary to Flanders (northern part of Belgium), Wallonia was not part of the COST 341-project ‘Habitat Fragmentation due to linear Transportation infrastructure’ (Damarad and Bekker 2003). Regionalisation of competency such as road and environment management in Belgium creates such reared situation where one part of the country might be involved in a project but not the other one. Currently, the only published data on WVC in Belgium is from Holsbeek et al. (1999) who showed how Belgian road network affects small to medium size mammals and birds. Particularly, this study showed the importance of hedgehog and red foxes in WVC, accounting for 60% of total animals found killed. Holsbeek et al. (1999) estimated the number of animal roadkills in Belgium to 4 000 000. The absence of more published results on road ecology in Belgium can also be explained by the scattered information between insurance companies, forest state, local authorities, police, hunters and environmentalists (personal observation).

With its 4.8km/km² of public roads, Southern Belgium (Wallonia) has one of the densest road networks of Western Europe combined to high human population density of 209 inhabitants per km² (SPW 2012). Although, the Belgian road network is already developed since decades and has not seen great changes lately, the car fleet number has hugely increased between 1977 (3 315 071 vehicles) and 2011 (6 861 777 vehicles). Due to a particularly dense road network (Figure 2), an increasing game species populations (red deer, roe deer and wild boar) and the current lack of available statistics, Wallonia is an interesting region to study the patterns of animal road accidents.

The aim of our research is toprovide the first results of spatial and temporal patterns of WVC for southern Belgium. In particular we analysed casualties regarding their specific composition (percentage of involved species), and for main game species (red deer Cervus elaphus, roe deer Capreolus capreolus, wild boar Sus scrofa and red fox Vulpes vulpes) causing most important social and economic damages, we analysed temporal (annually, monthly, daily and hourly) and spatial distribution patterns.

Our study area is the Walloon region, southern part of Belgium (50°30N, 4°45E, Figure 1). Climate is sub-oceanic temperate with a mean annual temperature of 8°C and a mean monthly temperature varying between 2 to 16°C. The mean annual rainfall is 900mm, and the mean annual length of snow cover is over 25 days. Roe deer, red deer and wild boar are the most abundant wild ungulates.

Data sources used to investigate accidents involving animals are manifold: insurance companies (Inbar et al. 2002; Vignon and Barbarreau 2008), local management authorities (Baker et al. 2004), traffic safety authorities (Colino-Rabanal et al. 2012; Diaz-Varela et al. 2011), systematic road monitoring (Hell et al. 2005) or police database (Balčiauskas and Balčiauskienė 2008). Apart from systematic observations on road sections, most of these methods cannot be considered as exhaustive since they are dependent on many factors (e.g. casualties’ record by police/insurance or volunteer participation in data retrieval) preventing collection of all WVC cases (personal observation). In our study, we opted for data from the police considering its availability and its relatively standardised collection protocol. Indeed, although the method is likely to underestimate the total number of accidents, as police is not called in every casualties cases, we assumed that impact with large mammals (wild boar, roe deer, red deer and red fox) have higher chance to end into important car damage requiring police intervention and report. We thus hypothesized that for the species of interest information from police provide the most representative dataset of the number of animal-related accidents.

We investigated police database to search for traffic accident involving any domestic or wild animal species. Information’s on accidents resulting in injuries or fatalities were directly available from the central police database, while for accidents resulting in car damages each paper statement had to be checked out individually. In total, the collected data cover the period between 2003 and 2011, and consist of 3965 accidents. For each event we recorded date, time, species involved, and information about the location when available. From the 3965 accidents, 51% could be georeferenced by their milestone reference or by a detailed description of the location with an accuracy of 100m. Milestone references or detailed location were then exported into X Y value by using the corresponding value from the Wallonia road agency.

Exploratory analysis aimed at identifying species involved by calculating absolute and relative frequency of WVC occurrence for each category of species.Types of roads on which accidents occurred were also identified. For the temporal and spatial patterns analysis we only considered the following main game species: red and roe deer, wild boar and red fox. The reason for this choice is twofold. Firstly, they are the main species involved in WVC with social and economic consequences for which mitigation measures are required. Secondly, data available in police database contain only cases where at least car damages occur, preventing exhaustive information on WVC with small mammals to be analysed.

Temporal analysis focused on annual, monthly, weekly and daily distribution patterns of WVC. For each of these timescale, we looked for differences in roadkills relative frequency by using chi-square goodness-of-fit test (Greenwood and Nikulin 1996). Using radar charts in Excel (Microsoft Corp. 1985–2007), we compared hourly distribution of accidents involving wild and domestic species (Lagos et al. 2012). We considered all the WVC from the study period together as we did not observe year effect on the temporal patterns.

For the spatial analysis we only consider accidents that occur along highways and national roads, due to the high complexity of the local road network and the relative low number of WVC compared to the total length of the road network. In total, we had thus a dataset composed of 840 casualties (Figure 3). Spatial pattern of WVC was assessed in three steps.

In a first step, we used nearest neighbour distance (NND) to assess the distribution of animal-related casualties along the road network (Gonser et al. 2009). For each species, we compared the observed mean distance of each accident to its nearest neighbour with the expected mean distance if accidents were randomly distributed along road (CRS, complete spatial randomness). NND analysis allows to determine whether WVC are aggregated or not along the road network. We compared observed value to 100 Monte Carlo simulations of a random distribution that allows estimating mean and confidence intervals at 5% for expected value. If the observed value is higher than the upper confident interval, points are aggregated, otherwise under the lower confidence interval, points are dispersed (Okabe et al. 2013). Departure from a random distribution was confirmed by calculating the Clark-Evans index which is the ratio of observed and expected mean distance, values >1 indicating points are aggregated, otherwise points are dispersed (Clark and Evans 1954).

In a second step, we identified hot spot of collisions for each species by means of a kernel density analysis (Bailey and Gatrell 1995; Okabe et al. 2009). Kernel density expresses the number of collision per kilometer of roads for all the of species of interest. Mapping kernel density allows identifying hotspot zones where mitigation measures should be set up. We used a bandwidth of 500m as the search radius for calculating the number of accidents, to estimate a density value. This value of 500m is reasonable to discriminate priority areas on which implement mitigation measures should be developed (Conruyt-Rogeon and Girardet 2012). Estimated densities were then classified using the Jenks methods, based on minimization and maximization of variance respectively, within and between density classes (Jenks and Caspall 1971).

In a third step, we examined spatial structure of WVC at various spatial scales using the Ripley K function describing distribution patterns of points in space at different scales (Mountrakis and Gunson 2009; Ripley 1976). Originally this function is used for 2-D applications. Here to consider 1-D issue of point distributed along a line, we used the Global auto K function provided by the SANET tools under ArcGIS 10 (Okabe and Yamada 2001). For each WVC cases, the number of neighbors WVC is calculated at spatial scales multiple of 500m along the road network. For detecting deviations from spatial homogeneity, we used the L-function, a variant of the Ripley’s K for testing range of spatial structure (Bailey and Gatrell 1995). This L-function is the difference between the observed and expected K-values at each spatial scale r (Clevenger et al. 2003; Langen et al. 2012) and presents the advantage compared to the K-function to be normalized at 0 (representing random distribution). Expected values (mean and 95% CI) were calculated using 100 Monte-Carlo simulations of random points distributed along the road network. Observed L(r) values were then plotted against the expected values. At each scaled distance r, the value of L(r) above or under the 95%CI revealed respectively an aggregated or a dispersed distribution of WVC along the road network.Due to the small number of WVC relative to full Walloon road network, we perform this third analysis on a sub-region that contains the highest density of WVC as indicated by the kernel density map.

All the spatial analyses were performed with the use of the extension SANET^® 4.1. Beta for ArcGIS 10 which provides tools adapted to perform spatial analysis along linear features (Okabe et al. 2006).

We observed that 87% of accidents with animals result in car damages, which is in accordance with a study by Williams and Wells (2005) who found a value of 80%. Most of these accidents are caused by wild animals which are in accordance with what has been observed in Europe and North American. Large mammals (over 30kg), usually ungulates, are indeed known to be more frequently involved in collisions with vehicles (Conover et al. 1995; Putman 1997). We didn’t achieve in deep review of the causal factors of accidents with domestic animal, however we might suspect escapement of cattle and dog as the main reason for impact with these animal on the roads. Appropriate fencing (preventing access of cattle, horses to the roads) as well as good handling of dogs could help decreasing casualties with domestic animals. Although large species are the main reasons for car accident, non-negligible part (7% of total accidents) is due to small species, and this percentage even reaches 17% when including dogs.

In Sweden wildlife-related accidents is suspected to cause an average annual loss of 1–12% of the estimated population of common mammal species (Seiler et al. 2004). On average, for the studied period, the proportion between accidents and hunting bag was 0.5% for wild boar, 0.22% for roe deer and 0.27% for red deer. If we assume hunting bag as an index of population number and the estimation from Seiler et al. (2004) to be similar in Wallonia, we can say that police data for these three species includes between 4–50% for the wild boar, 2–20% for roe deer and 2.5–27% for red deer of the total annual casualties with these wild species.

Our study is the first to bring results in Wallonia about WVC. We brought insights into species involved and in the spatio-temporal patterns of these accidents. Wild boar and roe deer constitute the major part of the accidents. Temporal analysis showed the role of animal biology in explaining monthly and daily patterns of WVC occurrences. Multi-scale spatial analysis demonstrated the clustering patterns of accidents in Wallonia for the main game species. These results highlight the need to take both spatial and temporal components into account for building predictive model. The next step of our research will indeed be dedicated to joining the results of this research together with landscape and road factors to build a predictive model of WVC hotspots and to propose mitigation strategies that could enhance both human and animal safety. GPS data collected on wild boar and red deer will be used to determine the road effect for these two species behaviour. This will bring us new understanding on the real effect of road network on animal behaviour. However many WVC are still not reported, because of the absence of police statement, but would be required to measure more precisely long–term effects of roads on main game species. We thus hope these first results will be helpful in convincing stakeholders (police, local authorities, insurance companies, forest administration, naturalists, drivers) to collaborate so that better data collection could be achieved in the near future.