Reindeer (Rangifer tarandus) in north Scandinavia are part of a traditional social-ecological system: while individually owned and at times herded and tended, reindeer roam free most of the year, largely using their natural and pre-historical migration routes and seasonal ranges. We here describe the case of Liehittäjä, a herd of some 1,200 reindeer dwelling an area of lowland taiga around 66°N near the Gulf of Bothnia. Reindeer in this herd migrate between summers inland and winters in the archipelago. The major transport infrastructures in the region however follow the coast, thereby intersecting reindeer´s migration routes. Within the past 20 years, the coastal highway has been upgraded (wider and higher speed limit) and a new high-speed railway constructed, both with fences. The railway has a dedicated reindeer overpass, plus 14 viaducts and bridges for streams or local traffic. The highway basically lacks safe passage opportunities for deer. Many reindeer manage to successfully cross the infrastructures via bridges, viaducts and a temporary fence opening at the highway, but deer are regularly caught between fences and some 30-50 are roadkilled annually. Reindeer herders make increasing efforts to guide deer through passages, drive them off the road, manage traffic victims, transport deer by truck past obstacles, and provide supplementary feed away from the road. By these adaptations, the migratory system endures, but with significant efforts and costs for both herders and animals. We emphasize the importance for infrastructure managers to address potential impacts on free-ranging domestic animals and on traditional husbandry.

While crossing structures are a common strategy to facilitate animals across fenced roads, they may not be feasible in certain sites due to cost and construction constraints. An alternative strategy could be an at-grade fauna passage coupled with Animal Detection and Driver Warning Systems (ADDWS), which allows animals to cross over specific sections of the road, while alerting drivers of animals at the upcoming passage, so that they can adjust their driving behaviour to avoid collisions. In this study, we investigated the use of one such site, by roe deer (Capreolus capreolus), red deer (Cervus elaphus) and wild boar (Sus scrofa) in southern Sweden. We found an overall 66 % reduction in the number of collisions at our site compared to unmitigated sections of road. Furthermore, when roadside verges are vegetated, ungulates often spend time browsing beside the road, thereby ‘encountering’ many vehicles, and make few attempts to cross the road. If vehicles pass through the fauna passage, animals are slower to cross roads and spend more time in the passage than when it is clear from traffic. When the grassy vegetation in the roadside verges are replaced by sand, animals spend less time in the fauna passage and have higher probability to cross the road directly, thereby encountering fewer vehicles. These results suggest that the combination of wildlife fence and at-grade fauna passages with ADDWS, particularly when roadside verge vegetation is minimized, can successfully reduce the risk of wildlife-vehicle collisions for medium -sized and large ungulates.

Transports contribute to the ongoing 6th mass extinction of species. They impact species viability by reducing the availability of suitable habitat, limiting connectivity between suitable patches, and increasing direct mortality due to collisions with vehicles. Not only does it represent a threat for some species conservation capabilities, but animal vehicle collisions (AVC) is also a threat for human safety and security in transport and has a massive cost for transport infrastructure (TI) managers and users. Using the opportunities offered by the increasing number of sensors embedded into TI and the development of their digital twins, we developed a framework aiming at managing AVC by mapping the collision risk between trains and ungulates (roe deer and wild boar) thanks to the deployment of a camera trap network. The proposed framework uses population dynamic simulations to identify collision hotspots and assist with the design of sensors deployment. Once sensors are deployed, the data collected, here photos, are processed through deep learning to detect and identify species at the TI vicinity. Then, the processed data are fed to an abundance model able to map species relative abundance of species around the TI as a proxy of the collision risk. We implement the framework on an actual section of railway in south-western France benefiting from a mitigation and monitoring strategy. The implementation thus highlighted the technical and fundamental requirements to effectively mainstream biodiversity concerns in the TI digital twins. This would contribute to the AVC management in autonomous vehicles thanks to connected TI.

More than 20% of the world’s terrestrial land is covered in roads, fragmenting habitats, reducing wildlife connectivity and resulting in millions of wildlife-vehicle collisions (WVC). Little is known, however, about how habitat fragmentation and WVC-risk intersect on a country-wide scale. We investigated “roadless areas” (areas at least 1km from a paved road) in the context of fragmentation, WVC-risk and species conservation within the United Kingdom (UK). Using generalized linear models, we tested for associations between roadless area distribution, size, land cover, and predicted WVC-risk for a focus species, the European badger Meles meles. We hypothesised that WVC-risk would be highest near smaller roadless areas, due to the greater density of roads. We find that the UK is broken up into 6,402 roadless areas by more than 400,000km of roads, with 71% of these areas being smaller than 1km². Roadless areas differed significantly in size between countries, with, on average, the smallest found in England (mean = 2.7km²), and the largest in Scotland (mean = 19.6km²). The most dominant land cover type across all roadless areas was acid grassland (~12500km²), a relatively low biodiversity habitat type, and 60% of the extent of roadless areas fell outside of protected areas. WVC-risk was significantly negatively correlated with roadless area size, with risk decreasing as roadless area size increased. Our results suggest that mitigation efforts should focus on the most fragmented regions, where WVC-risk is greatest. We also explore how roadless areas could be used to expand, and better connect, existing protected area networks.

Wildlife vehicle collisions (WVC) pose a significant threat to road safety in many regions of Europe. In-depth work has been done to identify and quantify the spatial and temporal factors involved. Although there appears to be a close relationship between hunting and the WVC occurrence, in reality, studies in this field have been limited. This research delved into this relationship through a detailed analysis of the results of hunting activities in the hunting grounds of Castilla y León, Spain (for the period September 2015 – February 2020) and the collision data extracted from accident reports compiled by the Spanish Directorate General of Traffic (for the same period). The aim was to determine which factors explain why a hunting ground is more or less likely to promote WVCs. Data analysis included variables such as hunting season, the size of the hunting grounds, the availability of refuge for wildlife, road density, or the presence of fences. The results show notable differences between hunting grounds, closely related to the seasonality of hunting activities and the availability of refuge/vegetation for wildlife.

The WiConNET project was created in 2017 by the Austrian Research Agency in cooperation with the Austrian Federal State Roads, the Austrian highway operator ASFINAG and the Austrian rail infrastructure provider OBB-INFRA to improve and validate passive and active wildlife protection measures at roads and railways. The project was concluded after 5 years in 2022.
Project results
• A study on mitigation measures currently in use to mitigate wildlife-vehicle-collisions (WVC) was complemented by a gap-analyse how to improve the state of the art.
• In a second stage, several new enhanced active electronic devices for road and rail have been developed. The development includes optic- and thermo-activated Virtual Fence devices, wirelessly connected devices, development of remote-trigger to detect high speed vehicles, and the development of gateways to transfer data from the various devices via internet to a central control server to monitor the status of the connected devices (temperature, charging status, luminance, etc).
• Then, about 1,000 active and passive devices were manufactured and deployed at 15 testsites across Austria to validate the effectiveness.
Results will be presented.
Highlights:
• Out of the WiConNET-project, new active electronic wildlife-vehicle-collision mitigation devices have been developed and validated. These so-called VirtualFence devices are now being transferred into commercially available products for rollout.
• In addition, we got a lot of insight how to deploy active and passive measures for best effectiveness and how to avoid several quite common mistakes.
• Finally, recommendations on rules for the setup of measures for road and rail have been forwarded to the Austrian RVS standardization committee.
Best practices for deployment will be presented.

Wildlife mortality resulting from collisions with vehicles is widely recognized as one of the primary adverse effects of roads on numerous species. Scientists and consultants have undertaken extensive road surveys, compiled data from opportunistic roadkill observations, and developed citizen science applications to better understand the mechanisms underlying such events and their potential implications for wildlife populations. However, a significant part of the literature addressing this issue, including research papers, dissertations, reports, and other forms of gray literature is either highly localized or lacks the display of geographic coordinates for roadkill locations. The objective of this presentation is to introduce the largest open-access dataset on roadkill data for terrestrial vertebrates. We conducted a comprehensive compilation of publications on road surveys and then invited the authors to share the location of roadkill incidents, date and species along with information on the type of survey conducted. A total of 163,271 roadkill records were compiled through the collaboration of more than 300 contributors from 50 countries across all continents, encompassing data collected between 1971 and 2024. We collected information on 119 threatened species with a total of 3965 records. Mammals have the highest number of roadkill records (63%), followed by amphibians (21%), reptiles (9%) and birds (7%). This global dataset will soon be published in several repositories (e.g. GBIF), and with it we aim to contribute for better understanding of wildlife-vehicle collisions impacts on wildlife populations, thereby accelerating scientific progress in the field of transport ecology.