1.In recent decades, the wild boar Sus scrofa has simultaneously increased its population size and colonized new habitats, causing more ecological and socio-economic concern than perhaps any other ungulate species. However, the drivers and mechanisms of the species' spatial ecology remain poorly understood. Thanks to a recently developed framework, the movement ecology of any organism can now be tackled within a consistent and unified theoretical approach. 2. Based on this framework, we reviewed the literature on wild boar movement ecology to assess current knowledge and to identify important gaps. 3. By using important navigational (e.g. olfactory sense) and cognitive (e.g. spatial memory, learning from conspecifics) abilities, wild boar have developed complex movement strategies to cope with external factors. However, there is a lack of detailed information on the role played by the internal state (motivation) and motion capacity in shaping the spatial ability of the species. 4. Specific aspects of the movement ecology of the wild boar, together with its high diet plasticity and its high prolificacy, are probably the most important causes of the rapid spread of wild boar worldwide. 5. We hope our review will inspire other scientists to apply their biological models to the movement ecology paradigms. Furthermore, we suggest that future researchers dealing with the movement ecology of any species should explicitly state the components and interactions of the framework investigated to facilitate further understanding and comparison among studies. bs_bs_banner Mammal Review ISSN 0305-1838 Fig. 1. The movement ecology framework, showing the four interacting components: motivation/internal state, motion capacity, navigation capacity and external factors.

This paper is the first to report data on wildlife-vehicle collisions (WVC) in Wallonia, southern Belgium, characterised by one of the densest road network worldwide. With the collaboration of police we identified 3965 accidents involving "free ranging animal" between 2003 and 2011. We observed that these accidents with free ranging animals result in 13% of cases in injuries for the drivers or passengers, and in less than 1% of cases in fatalities (death). 78% of these casualties involve wild animals, among which wild boar take the largest part (39%). During the covered period we observed an annual increase of WVC of 21%. For wild boar and red deer, this increase was significantly correlated with hunting statistics, used as an index of population density. The temporal analysis demonstrated an increase of WVC during night time with peak of accidents at dusk and dawn. Monthly distribution revealed the role of breeding, dispersal and hunting in shaping temporal patterns of accidents. Spatial analysis, focusing on wild boar, roe deer, red deer and red fox demonstrated clustering of accidents for all these species, until scale between 20 to 70 km. Mapping of accidents via Kernel density analysis permitted us to highlight areas with high risk of WVC risk. Our study suggests that the problem of car accidents due to wildlife is an increasing concern in Wallonia but results on spatial and temporal patterns should help for setting up mitigation measures in the most sensible areas. Moreover we suggest that police data source should be used for nationwide analysis and for comparison between countries.

African swine fever (ASF) is a fatal disease infectious to wild and domesticated suids. This disease entered the European Union in 2014 and recently reached western Europe, with the first cases observed in Belgium in September 2018. Carcasses of ASF‐infected wild boar play an important role in the spread and persistence of the virus in the environment. Thus, rapidly finding and removing carcasses is a crucial measure for effective ASF control. Using distribution modelling, we investigated whether the fine‐scale distribution of ASF‐infected animals can be predicted and support wild boar carcass searches. Our results suggest that ASF‐infected wild boar selected deathbeds in cool and moist habitats; thus, deathbed choice was mostly influenced by topographic and water‐dependent covariates. Furthermore, we show that in the case of an epidemic, it is important to quickly collect a minimum of 75–100 carcasses with exact locations to build a well‐performing and efficient carcass distribution model. The proposed model provides an indication of where carcasses are most likely to be found and can be used as a guide to strategically allocate resources.