Spatio-temporal patterns of wildlife-vehicle collisions in a region with a high-density road network

Identifying Roadkill Hotspots for Mammals in the Brazilian Atlantic Forest using a Functional Group Approach

A critical step to design wildlife mitigating measures is the identification of roadkill hotspots. However, the effectiveness of mitigations based on roadkill hotspots depends on whether spatial aggregations are recurrent over time, spatially restricted, and most importantly, shared by species with diverse ecological and functional characteristics. We used a functional group approach to map roadkill hotspots for mammalian species along the BR-101/North RJ, a major road crossing important remnants of the Brazilian Atlantic Forest. We tested if functional groups present distinct hotspot patterns, and if they converge into the same road sectors, in that case, favoring optimal mitigating actions. Roadkill rates were monitored and recorded between October/2014 and September/2018 and species were classified into six functional groups based on their home range, body size, locomotion mode, diet, and forest-dependency. Hotspots along the roads were mapped for comparison of spatial patterns between functional groups. Results demonstrated that the roadkill index varied idiosyncratically for each functional group throughout the months and that no group presented seasonality. Seven hotspots were shared by two or more functional groups, highlighting the importance of these road stretches to regional mammal fauna. Two of the stretches are associated with aquatic areas extending from one side of the road to the other, and the remaining are connected to patches of native vegetation on both sides. This work brings a promising approach, yet hardly used in ecological studies on roads to analyze roadkill dynamics, assigning more importance to ecological instead of taxonomical characteristics, normally used to identify spatiotemporal patterns.

Temporal and spatial patterns of small vertebrate roadkill in a supercity of eastern China

An assessment of animal roadkill can help develop road mitigation measures. This article is the first to report data on animal-vehicle collisions (AVCs) in Nanjing, a supercity in eastern China. The research was conducted on a 224.27 km stretch of nine roads in Nanjing. In the period, between November 2020 and October 2021, 26 fortnightly monitoring missions were conducted to gather roadkill carcasses so that we could analyze their temporal and spatial distribution patterns. A total of 259 carcasses were collected, comprising 22 different species, of which 46.42% were mammals and 48.81% were birds. Cats and dogs are the most roadkill mammals, and blackbirds and sparrows are the most roadkill birds. The temporal analysis demonstrated that the peak of vertebrate roadkill occurred from May to July. Spatial analysis showed that the distribution patterns of vertebrate roadkill on different roads varied with a generally non-random distribution and aggregation. By mapping accidents using kernel density analysis, we were able to pinpoint locations that were at high risk for roadkill. Due to the fortnightly survey, our results would underestimate the casualties, even if, our study suggests that the problem of car accidents due to animals should be a cause for concern, and the results of the analysis of temporal and spatial patterns contribute to the establishment of mitigation measures.

Wherever I may roam-Human activity alters movements of red deer (Cervus elaphus) and elk (Cervus canadensis) across two continents

Human activity and associated landscape modifications alter the movements of animals with consequences for populations and ecosystems worldwide. Species performing long-distance movements are thought to be particularly sensitive to human impact. Despite the increasing anthropogenic pressure, it remains challenging to understand and predict animals' responses to human activity. Here we address this knowledge gap using 1206 Global Positioning System movement trajectories of 815 individuals from 14 red deer (Cervus elaphus) and 14 elk (Cervus canadensis) populations spanning wide environmental gradients, namely the latitudinal range from the Alps to Scandinavia in Europe, and the Greater Yellowstone Ecosystem in North America. We measured individual-level movements relative to the environmental context, or movement expression, using the standardized metric Intensity of Use, reflecting both the directionality and extent of movements. We expected movement expression to be affected by resource (Normalized Difference Vegetation Index, NDVI) predictability and topography, but those factors to be superseded by human impact. Red deer and elk movement expression varied along a continuum, from highly segmented trajectories over relatively small areas (high intensity of use), to directed transitions through restricted corridors (low intensity of use). Human activity (Human Footprint Index, HFI) was the strongest driver of movement expression, with a steep increase in Intensity of Use as HFI increased, but only until a threshold was reached. After exceeding this level of impact, the Intensity of Use remained unchanged. These results indicate the overall sensitivity of Cervus movement expression to human activity and suggest a limitation of plastic responses under high human pressure, despite the species also occurring in human-dominated landscapes. Our work represents the first comparison of metric-based movement expression across widely distributed populations of a deer genus, contributing to the understanding and prediction of animals' responses to human activity.

High-Resolution Mapping of Material Stocks in Belgian Road Infrastructure: Material Efficiency Patterns, Material Recycling Potentials, and Greenhouse Gas Emissions Reduction Opportunities

Road infrastructure is an integral part of built environment stocks, as it delivers essential social and economic services. While previous work has assessed material stocks, flows, and embodied emissions, spatially refined mapping of materials accumulated in road infrastructure can highlight hitherto underappreciated synergies between improved spatial planning, material stock efficiency, and urban mining. In this study, we mapped the materials stocked in road infrastructure across Belgium, explored the patterns of material stock efficiency and the recyclability of end-of-life road materials, and examined the greenhouse gas (GHG) emissions reductions of improving stock efficiency and recycling. We assembled data scattered across various governmental sources and crowdsourced platforms and developed a comprehensive database to warehouse locational information on road typology, layer geometry and thickness, material characteristics, traffic volume, climatic conditions, and soil conditions. Our results reveal a strong but nonlinear correlation between material stock efficiency and population density, indicating that spatial planning can reduce the required road stocks and associated GHG emissions. Urban mining potentials in road infrastructure hinge on multiple factors, such as the proximity to recycling facilities and the degradation of pavements during use. Our counterfactual analysis shows that urban road planning and reusing recycled asphalt can cut GHG emissions by up to 53 and 70%, respectively. Therefore, material-efficient road planning and improved material recycling can help realize circular economy potentials and mitigate GHG emissions moving forward.

A spatiotemporal risk prediction of wildlife-vehicle collisions using machine learning for dynamic warnings

INTRODUCTION

The technology in the automotive industry is becoming increasingly safer in the age of automated driving, but the number of accidents is still high, especially in wildlife-vehicle collisions (WVCs). To better avoid these accidents, patterns involved in these accidents must be detected.

METHOD

This paper presents a spatiotemporal risk prediction of WVCs, including various road and environmental characteristics. A process of data preparation using GIS automated by Python scripts was developed to enable a spatiotemporal link of diverse features for the subsequent predictive data analysis. Different machine learning (ML) approaches were applied- random forest (RF), feedforward neural networks (FNN), and support vector machine classifier (SVM) - including automated ML to predict the risk of WVCs. Therefore, a dataset of approximately 731,000 accidents reported to the police in Bavaria over a period of 10 years (2010-2019) was used. In addition, non-accidents were randomly generated in Python over time and space for the supervised ML processes. As the actual risk probability for WVCs and non-WVCs is not entirely known, the impact of different training ratios between accidents and non-accidents was tested on the risk prediction quality (RPQ) (25%, 50%, 75%, 90% WVCs) of the double-weighted sensitivity and single-weighted specificity rate.

RESULTS

The test yielded high mean values of RPQ as an indicator for a suitable WVC prediction. Both RF (86.6%) and FNN (86.7%) were identified as suitable choices for WVC risk prediction in terms of RPQ. The SVM yielded a lower prediction quality, even though acceptable results could be achieved within a shorter runtime.

PRACTICAL APPLICATIONS

Spatial transferability was verified since the algorithm was trained on the dataset of Bavaria (excluding Upper Bavaria) and successfully tested in Upper Bavaria. WVC forecasts were also proven through training with datasets from 2010-2017 and in prediction for 2018 and 2019.

Using Self-Organizing Maps to find spatial relationships between wildlife-vehicle crashes and land use classes

The construction and expansion of roads cause significant impacts on the environment. The main potential impacts to biotic environment are vegetation suppression, reduction of the amount and composition of animal distribution due to forest fragmentation and increasing risks of animal (domestic and wildlife) vehicle collisions. The objective of this work was to establish a relationship between the different spatial patterns in wildlife-vehicle crash, by using spatial analysis and machine learning tools. Self-Organizing Maps (SOM), an artificial neural network (ANN), was selected to reorganize the multi-dimensional data according to the similarity between them. The results of the spatial pattern analysis were important to perceive that the point data pattern varies from an animal type to another. The events occur spatially clustered and are not uniformly distributed along the highway. SOM was able to analyze the relationship between multiple variables, linear and non-linear, such as ecological data, and established distinct spatial patterns per each animal type. In the studied area, most of the wildlife was run over very close to forest area and water bodies, and not so close to sugarcane fields, forestry and built environment. A considerable part of the wildlife-vehicle collisions occurred in areas with diverse landscape.

Modeling road traffic safety based on point patterns of wildlife-vehicle collisions

Wildlife-vehicle collisions represent one of the main coexistence problems that appear between human populations and the environment. In general terms, this affects road safety, wildlife management, and the building of road infrastructures. These accidents are a great danger to the life and safety of car drivers, cause property damage to vehicles, and affect wildlife populations. In this work, we develop a new approach based on algorithms used to obtain minimum paths between vertices in weighted networks to get the optimal (safest) route between two points (departure and destination points) in a road structure based on wildlife-vehicle collision point patterns together with other road variables such as traffic volume (traffic flow information), road speed limits, and vegetation density around roads. For this purpose, we have adapted the road structure into a mathematical linear network as described in the field of Graph Theory and added weights to each linear segment based on the intensity of accidents. Then, the resulting network structure allows us to consider some graph theory methodologies to manipulate and apply different calculations to analyze the network. This new approach has been illustrated with a real data set involving the locations of 491 wildlife-vehicle collisions in a square region (40 km × 40 km) around the city of Lleida, during the period 2010-2014, in the region of Catalonia, North-East of Spain. Our results show the usefulness of our new approach to model road traffic safety based on point patterns of wildlife-vehicle collisions. As such, optimal path selection on linear networks based on wildlife-vehicle collisions can be considered to find the safest path between two pairs of points, avoiding more dangerous routes and even routes containing hotspots of accidents.

Factors Affecting Roadkills of Semi-Aquatic Mammals

We previously showed that registration efforts and traffic intensity explain 90% of variation in mammal roadkill numbers, 70% of variation in the numbers of recorded species and 40% of diversity variation. Here we analyze semiaquatic mammal roadkill in Lithuania in 2002–2021, relating these to the monitoring effort. From 39,936 analyzed roadkill, the European beaver (Castor fiber) was registered 60 times, American mink (Neovison vison) 26 times, otter (Lutra lutra) 22 times and muskrat (Ondatra zibethica) 3 times. The average roadkill indexes were 0.000065, 0.00076, 0.00061 and 0.00010 ind./km/day, and the extrapolated annual roadkill for the country was 44–357, 36–456, 49–464 and 89–144 individuals, respectively. Beaver roadkill numbers correlated with the registration effort and traffic intensity, otter roadkill with registration effort only and mink with hunting bag (number of hunted individuals per year). Roadkill was not always related to proximity to water, with 38–54% of roadkill occurring over 200 m from the nearest water source. With American mink and muskrat being invasive species in the EU and otter protected in many countries, it is valuable to enhance the registrations of their roadkill (using targeted efforts by drivers, hunters or other citizen scientists) to obtain the extrapolated amount of roadkill and to use this knowledge in species management.

Animal-vehicle collisions during the COVID-19 lockdown in early 2020 in the Krakow metropolitan region, Poland

The interrelations between human activity and animal populations are of increasing interest due to the emergence of the novel COVID-19 and the consequent pandemic across the world. Anthropogenic impacts of the pandemic on animals in urban-suburban environments are largely unknown. In this study, the temporal and spatial patterns of urban animal response to the COVID-19 lockdown were assessed using animal-vehicle collisions (AVC) data. We collected AVC data over two 6-month periods in 2019 and 2020 (January to June) from the largest metropolis in southern Poland, which included lockdown months. Furthermore, we used traffic data to understand the impact of lockdown on AVC in the urban area. Our analysis of 1063 AVC incidents revealed that COVID-19 related lockdown decreased AVC rates in suburban areas. However, in the urban area, even though traffic volume had significantly reduced, AVC did not decrease significantly, suggesting that lockdown did not influence the collision rates in the urban area. Our results suggest that there is a need to focus on understanding the effects of changes in traffic volume on both human behaviour and wildlife space use on the resulting impacts on AVC in the urban area.

Temporal patterns of wildlife roadkill in the UK

Wildlife-vehicle collisions are one of the main causes of mortality for wild mammals and birds in the UK. Here, using a dataset of 54,000+ records collated by a citizen science roadkill recording scheme between 2014–2019, we analyse and present temporal patterns of wildlife roadkill of the 19 most commonly reported taxa in the UK (84% of all reported roadkill). Most taxa (13 out of 19) showed significant and consistent seasonal variations in road mortality and fitted one of two seasonal patterns; bimodal or unimodal: only three species (red fox Vulpes vulpes, European polecat Mustela putorius and Reeves’ muntjac deer Muntiacus reevesi) showed no significant seasonality. Species that increase movement in spring and autumn potentially have bimodal patterns in roadkill due to the increase in mate-searching and juvenile dispersal during these respective time periods (e.g. European badger Meles meles). Unimodal patterns likely represent increased mortality due to a single short pulse in activity associated with breeding (e.g. birds) or foraging (e.g. grey squirrels Sciurus carolinensis in autumn). Importantly, these patterns also indicate periods of increased risk for drivers, potentially posing a greater threat to human welfare. In addition to behaviour-driven annual patterns, abiotic factors (temperature and rainfall) explained some variance in roadkill. Notably, high rainfall was associated with decreased observations of two bird taxa (gulls and Eurasian magpies Pica pica) and European rabbit Oryctolagus cuniculus. By quantifying seasonal patterns in roadkill, we highlight a significant anthropogenic impact on wild species, which is important in relation to conservation, animal welfare, and human safety.

Sustainable Landscape Planning to Mitigate Wildlife–Vehicle Collisions

Road development, traffic intensification, and collisions with wildlife represent a danger both for road safety and species conservation. For planners, deciding which mitigation methods to apply is often problematic. Through a kernel density estimate, we analyzed 715 crossing locations and wildlife–vehicle collisions (WVCs) involving brown bears, lynx, wolf, red deer, roe deer, and wild boar in the Southeastern Carpathian Mountains. We identified 25 WVC hotspots, of which eight require urgent mitigation of existing infrastructure. Moreover, many of these hotspots are in Natura 2000 sites, along road sections where vegetation is in close proximity, animal movement is the highest, and driver visibility is low. Our study is the first in Romania to recommend practical solutions to remediate WVC hotspots and benefit sustainable landscape management.

Impact of Road Fencing on Ungulate–Vehicle Collisions and Hotspot Patterns

The number of road traffic accidents decreased in Lithuania from 2002 to 2017, while the ungulate–vehicle collision (UVC) number increased and accounted for approximately 69% of all wildlife–vehicle collisions (WVC) in the country. Understanding the relationship between UVCs, traffic intensity, and implemented mitigation measures is important for the assessment of UVC mitigation measure efficiency. We assessed the effect of annual average daily traffic (AADT) and wildlife fencing on UVCs using regression analysis of changes in annual UVCs and UVC hotspots on different categories of roads. At the highest rates, annual UVC numbers and UVC hotspots increased on lower category (national and regional) roads, forming a denser network. Lower rates of UVC increase occurred on higher category (main) roads, forming sparser road networks and characterized by the highest AADT. Before 2011, both UVC occurrence and fenced road sections were most common on higher-category roads. However, as of 2011, the majority of UVCs occurred on lower-category roads where AADT and fencing had no impact on UVCs. We conclude that wildlife fencing on roads characterized by higher speed and traffic intensity may decrease UVC numbers and at the same time shifting UVC occurrence towards roads characterized by lower speed and traffic intensity. Wildlife fencing re-allocates wildlife movement pathways toward roads with insufficient or no mitigation measures.

Lunar Phases and Wildlife-Vehicle Collisions: Application of the Lunar Disk Percentage Method

Simple Summary

The moon is ubiquitous in the night sky and considered an important abiotic factor that influences animal activity. However, little is known about the relationship between moonlight and the daily, monthly, or seasonal frequency of wildlife–vehicle collisions (WVCs). Traditionally, the influence of moonlight on WVCs has been analyzed using the lunar phase (quarters) approach, which evaluates moonlight on a rough scale (only four 25% steps of the visible moon disc and a strict arrangement of phases over time). We used a different approach; we compared WVCs to the actual lunar disc illumination that is based on the specific daily percentage of the visible lunar disk (LDP). Our findings indicated a significant trend of increasing WVC frequencies with increasing LDP at night. We also examined the correlation between the daily numbers of WVCs and LDP for different months and seasons. Positive correlations between LDP and WVCs were stronger at night and during the late autumn–winter months, particularly in December, suggesting the importance of lunar illumination on WVCs. Our study suggests that the LDP approach may provide more possibilities for the evaluation and quantification of WVCs and lunar light relationships than the traditional lunar phase approach. The results can be useful for predicting and reducing WVCs at different times of the lunar illumination cycle and in different seasons.

Abstract

We investigated the relationship between lunar illumination based on the percentage of the visible lunar disk (LDP) and the frequency of wildlife–vehicle collisions (WVCs) in Lithuania. We analyzed WVC frequency during ten 10% LDP intervals to more precisely reflect the relationship between LDP and WVC. The 10% LDP interval approach showed a significant trend of increasing WVC frequencies with an increasing LDP at night. We also examined the correlation between the daily numbers of WVCs and LDP for different months and seasons. The relationship seemed to be stronger at night and during the late autumn–winter months, particularly in December, suggesting the importance of lunar illumination on WVCs. There was a weak positive correlation between LDP and overall daily number of WVCs (r_s = 0.091; p < 0.001) and between LDP and night WVCs (r_s = 0.104; p < 0.001). We found significant positive correlations for winter (December–February) (r_s = 0.118; p = 0.012) and autumn (August–November) (r_s = 0.127; p = 0.007). Our study suggests that the LDP interval approach may provide more possibilities for the evaluation and quantification of WVCs and lunar light relationships than the traditional lunar phase approach.

Application of long-term collected data for conservation: Spatio-temporal patterns of mortality in Japanese serow

Monitoring the mortality of wildlife provides basic demographic information to support management plan preparation. The utility of mortality records for conservation measures was investigated in the Japanese serow, focusing on temporal trends and spatial distribution. Using the mortality records of Japanese serow from 2006 to 2018 in Gifu prefecture, cause-specific mortality was categorized into five groups (disease, accident, vehicle collision, parapoxvirus infection, and unknown), and the sex ratios were examined. A state space model was used to analyze the time series for the monthly mortalities, and kernel estimation was used for the spatial distribution of the parapoxvirus infection. Land cover type around the detection point was also reported. Disease, accident, and vehicle collision mortality were similar, and 30% of mortality was of anthropogenic origin. The number of mortality records for males was higher, and the larger home range of males could account for this. The state space model showed moderate increases in monthly mortalities over time and a seasonal variation with the highest level in spring and lowest in winter. Land cover analysis demonstrated a temporal increase in the proportion of human settlement areas, suggesting the change of the Japanese serow habitat. The proximity of Japanese serow to human settlements contributed to increase in mortality records. The point pattern analysis indicated spatial clustering for parapoxvirus infection in the area where an epidemic had occurred in the past. Several measures are recommended; however, mortality records can help develop improved conservation plan.

Life in the fast lane: roadkill risk along an urban–rural gradient

Wildlife-vehicle collisions are a major cause of mortality in animal populations and can cause significant population-level effects. Urban areas are typically associated with higher road densities and unique wildlife communities in comparison to rural areas, and therefore have the potential to be associated with high numbers of collisions, and roadkill risk. Here, we use a citizen science database of wildlife roadkill and species distribution models to assess how roadkill risk (probability of roadkill observation per km2) varied along an urban–rural gradient for British wildlife. Roadkill risk was positively associated with road density, until around 5000 m/km2, a value representing villages or the outskirts of towns and cities. Beyond 5000 m/km2, risk remained high for some species (hedgehog, fox, pigeons and gulls) but reduced for other species (badger, rabbit, pheasant). Roadkill risk was a function of live species distribution for badger, hedgehog and rabbit, with significant overlap between spatial patterns of roadkill risk and the species’ live distribution. This was not the case for fox, pheasant, pigeons and gulls. Fox roadkill risk was underrepresented in rural areas, possibly due to low road density, while pheasant risk was overrepresented. For pigeons and gulls—well-known urban exploiters—roadkill risk was overrepresented in urban areas given their live distributions, possibly due to risks associated with foraging, particularly roadkill scavenging by gulls. Our results highlight the dangers of the UK’s dense road networks to wildlife, even to species considered adapted to urban environments and human disturbance.

Developing a correction factor to apply to animal–vehicle collision data for improved road mitigation measures

Abstract ContextRoad mitigation to reduce animal–vehicle collisions (AVCs) is usually based on analysis of road survey animal carcass data. This is used to identify road sections with high AVC clusters. Large mammals that are struck and die away from a road are not recorded nor considered in these analyses, reducing our understanding of the number of AVCs and the cost–benefit of road mitigation measures. AimsOur aim was to develop a method to calculate a correction factor for large mammal carcass data reported through road survey. This will improve our understanding of the magnitude and cost of AVCs. MethodCitizen scientists reported animal carcasses on walking surveys along transects parallel to the highway and reported observations using a smartphone application at three sites over a 5-year period. These data were compared with traditional road survey data. Key resultWe found that many large mammals involved in AVCs die away from the road and are, therefore, not reported in traditional road surveys. A correction factor of 2.8 for our region can be applied to road survey data to account for injury bias error in road survey carcass data. ConclusionsFor large mammals, AVCs based on road survey carcass data are underestimates. To improve information about AVCs where little is known, we recommend conducting similar research to identify a correction factor to conventionally collected road survey carcass data. ImplicationsIdentifying road mitigation sites by transportation agencies tends to focus on road sections with above-threshold AVC numbers and where cost–benefit analyses deem mitigation necessary. A correction factor improves AVC estimate accuracy, improving the identification of sites appropriate for mitigation, and, ultimately, benefitting people and wildlife by reducing risks of AVCs.

Temporal patterns of ungulate-vehicle collisions in Lithuania

Wildlife mortality caused by vehicles is a serious conservation and economic problem as collisions with large mammals are global, pervasive and increasing. We analysed 14,989 reports of ungulate-vehicle collisions (UVC) that occurred in Lithuania from 2002 to 2017. We analysed UVC data for four major ungulate species (roe deer, red deer, moose and wild boar) and checked for potential seasonal or daily trends. The temporal distribution of collisions was species-dependent. UVC analysis showed strong monthly and hourly pattern. Most occurrences took place before or during sunrise (dawn) and after or during sunset (dusk) during the year. In spring, the highest UVC peaks occurred early in the morning and late in the evening, while in winter these peaks occurred in late mornings and early evenings. With most UVC occurring on Fridays, daily variations were weak. We conclude that temporal variations of UVC distributions are result of a complex interaction of phenological factors and animal behaviour. The information provided in this study reinforces the knowledge on the dynamics and patterns of UVC and represents an important element for the identification of mitigation measures. Our findings suggest that efforts to reduce UVC should also focus on driver attitudes considering the seasonal and daily variations in UVC.

Look both ways: factors affecting roadkill probability in Blue-black Grassquits (Volatinia jacarina)

The contribution of roads to human economic and social development contrasts with its impact on the environment and wildlife. One of the most significant impacts of roads is wildlife–vehicle collisions. Millions of individuals from numerous species are killed annually around the world. Here we investigated the spatial and temporal dynamics of road killing on a small neotropical bird, the Blue-black Grassquit (Volatinia jacarina (Linnaeus, 1766)). We used a data set of roadkill records collected between 2010 and 2015 to test the hypotheses that roadkills are concentrated during the breeding period of the species and that road features and weather conditions affect the likelihood of animal–vehicle collisions. We observed that the number of fatalities was temporally and spatially clustered. Roadkills were more frequent in warmer, rainy days with lower wind speed. Fatalities were more commonly associated with two-lane roads compared with dirt and four-lane roads. Given that Blue-black Grassquits are attracted to human-modified habitats, especially to artificial grasslands composed of exotic grasses usually found along the margins of roads, mitigation measures should focus on the management and control of grass populations. Roadside mowing may reduce areas where Blue-black Grassquits can establish territories, and consequently, reduce the activity of the species near roads.

Impacts and Potential Mitigation of Road Mortality for Hedgehogs in Europe

Transport infrastructure is a pervasive element in modern landscapes and continues to expand to meet the demands of a growing human population and its associated resource consumption. Road-induced mortality is often thought to be a major contributor to the marked declines of European hedgehog populations. This review synthesizes available evidence on the population-level impacts of road mortality and the threat to population viability for the five hedgehog species in Europe. Local and national studies suggest that road mortality can cause significant depletions in population sizes, predominantly removing adult males. Traffic collisions are a probable cause of fragmentation effects, subsequently undermining ecological processes such as dispersal, as well as the genetic variance and fitness of isolated populations. Further studies are necessary to improve population estimates and explicitly examine the consequences of sex- and age-specific mortality rates. Hedgehogs have been reported to use crossing structures, such as road tunnels, yet evaluations of mitigation measures for population survival probability are largely absent. This highlights the need for robust studies that consider population dynamics and genetics in response to mitigation. In light of ongoing declines of hedgehog populations, it is paramount that applied research is prioritised and integrated into a holistic spatial planning process.

How climate change and wildlife management affect population structure in wild boars

Global climate change affects many species and contributes to the exceptional population growth of wild boar populations and thus to increasing human-wildlife conflicts. To investigate the impact of climate change on wild boar populations we extended existing models on population dynamics. We included for the first time different juvenile conditions to account for long-lasting effects of juvenile body mass on adult body mass and reproductive success. Our analysis shows that incorporating phenotypes, like body mass differences within age classes, has strong effects on projected population growth rates, population structures and the relative importance of certain vital rates. Our models indicated that an increase in winter temperatures and food availability will cause a decrease in mean body mass and litter size within Central European wild boar populations. We further analysed different hunting regimes to identify their effects on the population structure as well as their efficiency in limiting population growth. While targeting juveniles had the lowest effect on population structure, such strategies are, however, rather ineffective. In contrast, culling predominantly yearlings seems very effective. Despite being equally effective, only focusing on adults will not result in a reduction of population size due to their low proportion within populations.

In the wrong place at the wrong time: Moose and deer movement patterns influence wildlife-vehicle collision risk

Mitigation strategies for wildlife-vehicle collisions require sufficient knowledge about why, where and when collisions occur in order to be an efficient tool to improve public safety. Collisions with cervids are known to be influenced by spatial factors such as topography and forest cover. However, temporal changes in animal and motorist behaviors are often overlooked although they can increase the odds of cervid-vehicle collisions. Consequently, we evaluated potential factors influencing the spatiotemporal distribution of 450 collisions with moose and white-tailed deer that occurred between 1990 and 2015 along the 100-km long highway in southeastern Québec, Canada. Both spatial and temporal factors efficiently explained moose-vehicle collisions but not collisions with white-tailed deer, suggesting that the latter occurred more randomly along the highway. The risk of moose-vehicle collisions was mainly modulated by topographic and habitat variables, as the interactions between slope and elevation and slope and distance to suitable moose habitats had a strong effect on collision risk. Road sinuosity and the proportion of mature coniferous stands around the collision site positively influenced deer-vehicle collisions. A temporal increase in collision numbers was noted in different biological periods during which movement rates are known to be higher (e.g. post-winter dispersal and rut). These results suggest that cervid movement is the main factor influencing collision risk and frequency. Our results indicate that mitigation strategies aimed at decreasing the probability of collision with cervids must be species-specific and should focus more closely on animal movement.

The ecology of human-nature interactions

The direct interactions between people and nature are critically important in many ways, with growing attention particularly on their impacts on human health and wellbeing (both positive and negative), on people's attitudes and behaviour towards nature, and on the benefits and hazards to wildlife. A growing evidence base is accelerating the understanding of different forms that these direct human-nature interactions take, novel analyses are revealing the importance of the opportunity and orientation of individual people as key drivers of these interactions, and methodological developments are increasingly making apparent their spatial, temporal and socio-economic dynamics. Here, we provide a roadmap of these advances and identify key, often interdisciplinary, research challenges that remain to be met. We identified several key challenges, including the need to characterize individual people's nature interactions through their life course, to determine in a comparable fashion how these interactions vary across much more diverse geographical, cultural and socio-economic contexts that have been explored to date, and to quantify how the relative contributions of people's opportunity and orientation vary in shaping their nature interactions. A robust research effort, guided by a focus on such unanswered questions, has the potential to yield high-impact insights into the fundamental nature of human-nature interactions and contribute to developing strategies for their appropriate management.

BOARD INVITED REVIEW: Immunocontraception as a possible tool to reduce feral pig populations: recent and future perspectives

The feral pig populations of many countries continue to increase. Scientific studies on population size are scarce, while the numbers of reported observations on presence of and damage caused by feral pigs are increasing. Feral pigs can carry and spread several diseases (including zoonotic), but African Swine Fever (ASF) is of most concern. It is a highly transmissible viral disease associated with an extremely high mortality rate. Since 2009 ASF has appeared in several European countries, with cases being identified first among local feral pigs and consequently in domestic pig production units, indicating a clear linkage with the movement of the feral pig population and the spread of the disease across national boundaries. Control of feral pig populations is currently under discussion. Because massive culling raises questions of animal welfare and ethics, fertility control could represent an important and effective means to control feral pig populations. Contraceptive vaccines have been used with some degree of success in many wild species because they are able to provide a long-term effect without any consequent health problems. However, extensive and efficacious use of vaccines to control feral pig populations is not simple. The aim of this article was to review the progress in immunocontraception use in feral pigs, providing an account of the current status and future perspectives.

On reliable identification of factors influencing wildlife-vehicle collisions along roads

Wildlife-vehicle collisions (WVCs) pose a serious global issue. Factors influencing the occurrence of WVC along roads can be divided in general into two groups: spatially random and non-random. The latter group consists of local factors which act at specific places, whereas the former group consists of globally acting factors. We analyzed 27,142 WVC records (roe deer and wild boar), which took place between 2012 and 2016 on Czech roads. Statistically significant clusters of WVCs occurrence were identified using the clustering (KDE+) approach. Local factors were consequently measured for the 75 most important clusters as cases and the same number of single WVCs outside clusters as controls, and identified by the use of odds ratio, Bayesian inference and logistic regression. Subsequently, a simulation study randomly distributing WVC in clusters into case and control groups was performed to highlight the importance of the clustering approach. All statistically significant clusters with roe deer (wild boar) contained 34% (27%) of all records related to this species. The overall length of the respective clusters covered 0.982% (0.177%) of the analyzed road network. The results suggest that the most pronounced signal identifying the statistically significant local factors is achieved when WVCs were divided according to their occurrence in clusters and outside clusters. We conclude that application of a clustering approach should precede regression modeling in order to reliably identify the local factors influencing spatially non-random occurrence of WVCs along the transportation infrastructure.

Wildlife warning reflectors do not mitigate wildlife-vehicle collisions on roads

Wildlife-vehicle collisions cause human fatalities and enormous economic and ecological losses on roads worldwide. A variety of mitigation measures have been developed over the past decades to separate traffic and wildlife, warn humans, or prevent wildlife from entering a road while vehicles are passing by, but only few are economical enough to be applied comprehensively. One such measure, wildlife warning reflectors, has been implemented over the past five decades. However, their efficacy is questioned because of contradictory study results and the variety of applied study designs and reflector models. We used a prospective, randomized non-superiority cross-over study design to test our hypothesis of the inefficacy of modern wildlife warning reflectors. We analyzed wildlife-vehicle collisions on 151 testing sites of approximately 2 km in length each. During the 24-month study period, 1984 wildlife-vehicle collisions were recorded. Confirmatory primary and exploratory secondary analyses using a log-link Poisson mixed model with normal nested random intercepts of observation year in road segment, involved species, and variables of the road segment and the surrounding environment showed that reflectors did not lower the number of wildlife-vehicle collisions by a relevant amount. In addition, variables of the road segment and the surrounding environment did not indicate differential effects of wildlife warning reflectors. Based on our results, we conclude that wildlife warning reflectors are not an effective tool for mitigating wildlife-vehicle collisions on roads.

Dogs are the main species involved in animal-vehicle collisions in southern Spain: Daily, seasonal and spatial analyses of collisions

Animal-vehicle collisions have become a serious traffic safety issue. Collisions have steadily increased over the last few decades, as have their associated socio-economic costs. Here, we explore the spatial and temporal patterns of animal-vehicle collisions reported to authorities in the province of Seville, southern Spain. Most animal-vehicle collisions involved domestic animals (>95%), particularly dogs (>80%), a pattern that sharply contrasts with that found in other Spanish and European regions, where collisions are mostly caused by game species. Dog-vehicle collisions were related to the traffic intensity of the roads and they were more frequent around dawn and dusk, coinciding with the peaks of activity of dogs. This pattern was consistent throughout the week, although on weekends there were fewer collisions due to lower traffic density at those times. These findings suggest that the aggregation of dog-vehicle collisions around twilight likely resulted from a combined effect of the activity peaks of dogs and traffic density. Seasonally, collisions increased in autumn and winter, coinciding with the period of intense hunting activity in the region. Further, during autumn and winter, rush hour partly overlaps with twilight due to longer nights in comparison with summer and spring, which may contribute to the increased rate of dog-vehicle collisions in these seasons. Spatially, satellite images of nighttime lights showed that dog-vehicle collisions were clustered near urban areas. Overall, the high incidence of stray dogs involved in animal-vehicle collisions highlights a road safety issue with this type of animals in the region.

Temporal patterns of moose-vehicle collisions with and without personal injuries

Collisions with wild ungulates are an increasing traffic safety issue in boreal regions. Crashes involving smaller-bodied deer species usually lead to vehicle damage only, whereas collisions with a large animal, such as the moose, increase the risk of personal injuries. It is therefore important to understand both the factors affecting the number of moose-vehicle collisions (MVCs) and the underlying causes that turn an MVC into an accident involving personal injuries or fatalities. As a basis for temporal mitigation measures, we examined the annual and monthly variation of MVCs with and without personal injuries. Using a 22-year-long (1990-2011) time series from Finland, we tested the effect of moose population density and traffic volume on the yearly number of all MVCs and those leading to personal injuries. We also examined the monthly distribution of MVCs with and without personal injuries, and contrasted the Finnish findings with collision data from Sweden (years 2008-2010) and Norway (years 2008-2011). Both moose population abundance indices and traffic volume were positively related to the yearly variation in the number of MVCs in Finland. The proportion of MVCs involving personal injuries decreased during our 22-year study period. The monthly distribution of all MVCs peaked during the autumn or winter depending on country, while MVCs involving personal injury peaked in summer. Our study indicates that efforts to reduce MVCs involving personal injuries need to address driver awareness and attitudes during summer, despite most MVCs occurring in autumn or winter.

Assessment of ecological passages along road networks within the Mediterranean forest using GIS-based multi criteria evaluation approach

Major roads cause barrier effect and fragmentation on wildlife habitats that are suitable places for feeding, mating, socializing, and hiding. Due to wildlife collisions (Wc), human-wildlife conflicts result in lost lives and loss of biodiversity. Geographical information system (GIS)-based multi criteria evaluation (MCE) methods have been successfully used in short-term planning of road networks considering wild animals. Recently, wildlife passages have been effectively utilized as road engineering structures provide quick and certain solutions for traffic safety and wildlife conservation problems. GIS-based MCE methods provide decision makers with optimum location for ecological passages based on habitat suitability models (HSMs) that classify the areas based on ecological requirements of target species. In this study, ecological passages along Motorway 52 within forested areas in Mediterranean city of Osmaniye in Turkey were evaluated. Firstly, HSM coupled with nine eco-geographic decision variables were developed based on ecological requirements of roe deer (Capreolus capreolus) that were chosen as target species. Then specified decision variables were evaluated using GIS-based weighted linear combination (WLC) method to estimate movement corridors and mitigation points along the motorway. In the solution process, two linkage nodes were evaluated for eco-passages which were determined based on the least-cost movement corridor intersecting with the motorway. One of the passages was identified as a natural wildlife overpass while the other was suggested as underpass construction. The results indicated that computer-based models provide accurate and quick solutions for positioning ecological passages to reduce environmental effects of road networks on wild animals.

Wild boar populations up, numbers of hunters down? A review of trends and implications for Europe

Across Europe, wild boar numbers increased in the 1960s-1970s but stabilised in the 1980s; recent evidence suggests that the numbers and impact of wild boar has grown steadily since the 1980s. As hunting is the main cause of mortality for this species, we reviewed wild boar hunting bags and hunter population trends in 18 European countries from 1982 to 2012. Hunting statistics and numbers of hunters were used as indicators of animal numbers and hunting pressure. The results confirmed that wild boar increased consistently throughout Europe, while the number of hunters remained relatively stable or declined in most countries. We conclude that recreational hunting is insufficient to limit wild boar population growth and that the relative impact of hunting on wild boar mortality had decreased. Other factors, such as mild winters, reforestation, intensification of crop production, supplementary feeding and compensatory population responses of wild boar to hunting pressure might also explain population growth. As populations continue to grow, more human-wild boar conflicts are expected unless this trend is reversed. New interdisciplinary approaches are urgently required to mitigate human-wild boar conflicts, which are otherwise destined to grow further.

Road and traffic factors correlated to wildlife–vehicle collisions in Galicia (Spain)

Context Wildlife–vehicle collisions (WVC) are one of the major risk factors for the safety of drivers, as well as a great danger to wildlife that moves through the territory. In recent decades, given the growth of these accidents, some researches emerged to understand what are the main causes of this phenomenon and find the best solutions for implementation and try to solve this problem. Aims The aim of the present study was to analyse the road and traffic characteristics of road segments with a high occurrence of WVC in north-western Spain, specifically, the collisions with wild ungulates (roe deer and wild boar). Methods A nearest-neighbour analysis was used to analyse the spatial distribution of the WVC spots, and so as to identify these hotspots of accidents, we performed a hotspot analysis using the routine nearest-neighbour hierarchical cluster. Then, we calculated the WVC density of each road segment (KP). The existence of differences in the values of variables between high and low accident densities was analysed using a Mann–Whitney U-test for the continuous variables, and a χ2-test for the categorical ones. Then, multiple logistic regression analysis was used to identify which variables could predict the existence of KPs with a high density of WVC. Key results Our results showed that the daily traffic volume, the width of the road, the number of lanes and speed limit affect whether a particular road marker has a high or low density of WVC. Conclusions We conclude that high WVC is frequently characterised by wider lanes and shoulders, as well as gentler slopes, whereas in the sections with narrower roads and a shorter curvature radius, there are some conditions (low visibility and speed reduction) that reduce the probability of having an accident with ungulates. However, the speed at which it is possible to drive on a given road section is closely related to the occurrence of WVC. Implications These findings emphasise the importance of including mitigation measures in the decision-making when planning and designing infrastructure.