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Aim Roads impact wildlife in different ways, among which road mortality has been the most studied. Budgets in conservation biology are usually small, and macroecological approaches have been employed in recent years as the first steps towards guiding management. Carnivores are particularly vulnerable to mortality on roads due to their elevated ecological needs (low population density, often low fecundity and relatively large home ranges). Our aim was to develop a ranking methodology to prioritize specific areas for road‐kill mitigation. Location Continental Italy. Methods We studied 271 occurrences of live polecats (Mustela putorius) and 212 polecat road‐kill sites. We used the former to generate a species distribution model and the latter to identify the variables that determined the road‐kill risk. Habitat suitability was derived from a spatial distribution model that combined the polecat occurrence data with a set of environmental variables. Prey availability was derived from the combination of suitability maps of 26 prey species. We used generalized linear modelling to identify the set of variables that best explained the occurrence of road‐kills. The variables included in the best performing model were combined to produce the road risk map and to identify the areas with the highest densities of road sections with highest risk. Results Road‐kills were positively associated with the road sections with higher broad‐leaved forest coverage. The number of casualties was found to be higher than expected on the national and provincial roads and lower than expected on the local roads. Main conclusions This approach allowed us to identify the 10 × 10 km cells where mitigation actions to prevent road‐kills should be prioritized. As mitigation actions (wildlife passage construction, fencing) are expensive, measures should be prioritized on the specific high‐risk road sections inside these selected cells, avoiding generalized mitigation plans.

The development and expansion of road networks have profoundly impacted the natural landscape and various life forms. Animals are affected by these roads in a myriad of ways, none as devastating as road mortalities. This article reviews the literature on the magnitude, spatiotemporal patterns, factors, and consequences of Animal-Vehicle Collisions (AVCs) and the subsequent road kills. Furthermore, the review paper briefly outlines the relationship between roads and animals in the surrounding landscape and later examines the nature and impacts of AVCs. This article evaluates the statistics on the number of road kills and a critical analysis of the spatiotemporal patterns of these mortalities is also evaluated. Subsequently, the review paper examines current mitigation measures and the challenges impeding their success. The paper then concludes with an evaluation of geospatial tools (GIS) and other technologies used in road kill studies. The relevant findings of this paper are that, (1) factors influencing road kill patterns interact with one another; (2) AVCs have serious environmental, economic and social consequences; (3) road kill mitigation strategies suffer several challenges hindering their success; and (4) specific geospatial tools and other technologies have been utilised in assessing AVC road kill patterns. The review, therefore, recommends including overall road kill clusters of all animals in mortality surveys, increasing the spatial coverage of road kill observations, consistent surveying, sufficient research on nighttime driving distances and speed, utilising citizen science in all road mortality studies and incorporating GIS into all apps used for recording road kills. An increased sufficiency in road kill data coupled with improved technologies can enable more effective mitigation strategies to prevent AVCs.

ContextAccurate spatiotemporal modeling of roadkill hotspots is essential for the assessment of high risk roadkill locations. Increasing the spatiotemporal resolution of models may facilitate greater cost-effective solutions for roadkill mitigation strategies.ObjectiveThis study develops a novel spatiotemporal roadkill distribution model to simulate roadkill probability. Moreover, we systematically identify top prioritized road segments by the most frequent roadkill occurrence for multiple focal species.MethodsBased on the theory of the Poisson process, the proposed spatiotemporal roadkill distribution model with seasonal effects is validated with four focal reptilian species. The model simulates spatiotemporal roadkill patterns and addresses uncertainty by referencing ensemble species distribution models. Finally, we systematically prioritize road segments by the most frequent roadkill occurrence for multiple focal species.ResultsThe efficacy of the proposed spatiotemporal roadkill distribution model which is validated in terms of the area under the receiver operating characteristic curve (AUC) and accurate proportions. The AUC values based independent roadkill data tests ranged from 0.73 to 0.84. Both the efficacy of the proposed model, and the increases in uncertainty are attributable to decreasing seasonal sampling size and variation. Based on the independent roadkill data, more than 70% of roadkill events occurred within the top 30% priority segments by our approaches.ConclusionsThe proposed model is successfully applied in simulation of spatiotemporal roadkill probability. The seasonal effects benefit identification of high roadkill probability. Through the systematic identification and the proposed model, our approach provides useful information for the design of cost-effective surveys and appropriate conservation planning and mitigation strategies.

Road-kill represents a major threat for butterflies and more generally for pollinators. Here we report an observation of conspicuous aggregations of butterflies mud-puddling on roadsides and, for this reason, being massively road-killed by farm vehicles.Implications for insect conservationWhile the reported observation by itself may not entail a significant threat to the populations of the observed species, it provides the opportunity to discuss an overlooked ecological trap, potentially affecting butterflies and especially threatened or endemic species. Indeed, this kind of mortality, due to a very common behaviour in butterflies, could affect any species in any area, and for this reason should be furtherly investigated and, when necessary, appropriately mitigated. Mitigation actions should prevent the formation of moist surfaces along roadsides, and in case of wide verges, provide artificial mud-puddling sites away from roads, in correspondence with the ecotone between roadside and matrix habitat.

Urban roads are known to affect wildlife fauna but most assessments of the impacts of roads have been done in cities of the developed world with comparable studies still lacking from sprawling cities of the developing countries. This gap precludes the ability of the city management authorities in designing the appropriate mitigation and conservation measures especially during this era where the road networks in African cities is expanding steadily. We surveyed 48 km of roads transcending an urban-rural gradient in Morogoro city, Tanzania to understand the patterns of road kills, taxonomic composition and used the Generalized linear modeling to determine the ecological and environmental factors mostly influencing the road kill abundances. We also assessed the conservation status of the road kills to propose measures to improve biodiversity conservation in this urban landscape bordering a global biodiversity hotspot. We found 929 killed animals belonging to 62 families and 23 orders and 5 taxa (classes) with the majority kills being insects. There was a significant difference on road kill abundance between taxa but no significant difference in kill abundance across the urban-rural gradient. Furthermore, we found that designated road speed limit was significantly positively associated with increased road kills with the insect taxon occurring most abundant in the kill. Additionally, we found three species involved in the animal-vehicle collision threatened with extinction and over 50% of the recorded road kills lacking information on their conservation status on the red list at all. These data may be useful in improving the strategies to reducing the animal-vehicle collisions and to inform the potential biodiversity monitoring in the study area and elsewhere in Africa’s cities faced with similar urbanization challenges.

In conservation, there is a high demand for methods to predict how animals respond to human infrastructure, such as estimating the location of road mortalities and evaluating the effectiveness of mitigation measures. Computer-based simulation models have emerged as an important tool in understanding wildlife-infrastructure interactions. Such models, however, often assume animal omniscience of the landscape yielding unrealistic movements, focus more on genetic connectivity than actual movement paths, or are case-specific and mathematically/computationally challenging to apply. Here, we illustrate the potential of SiMRiv, a novel R package for simulating spatially explicit, individual multistate (Markovian) movements incorporating landscape heterogeneity, in the subject of road ecology. In particular, we used SiMRiv to reproduce wildlife movement patterns and predict high-risk areas for road-kill, using Eurasian otters (Lutra lutra) as a model species. We compared the number of road crossings in real otter movements and null models (simulated, multistate Markovian movements) incorporating the effect of the landscape structure (here, water dependence). The number of road crossings in real and simulated movements was remarkably similar, and available limited real road-kill data supported SiMRiv’s road-kill risk predictions. Further, other emergent movement properties were also very similar in real and simulated movements. Overall, results show that SiMRiv has potential for reconstructing real wildlife movement patterns, as well as for predicting road-kill risk areas. SiMRiv constitutes a flexible, powerful, and intuitive tool to help biologists and managers to test mechanistic hypotheses on wildlife movement ecology, including those related to wildlife-vehicle interactions.

Most urban mammals are small. However, one of the largest marsupials, the Eastern Grey Kangaroo Macropus giganteus, occurs in some urban areas. In 2007, we embarked on a longitudinal study of this species in the seaside town of Anglesea in southern Victoria, Australia. We have captured and tagged 360 individuals to date, fitting each adult with a collar displaying its name. We have monitored survival, reproduction and movements by resighting, recapture and radio-tracking, augmented by citizen science reports of collared individuals. Kangaroos occurred throughout the town, but the golf course formed the nucleus of this urban population. The course supported a high density of kangaroos (2–5/ha), and approximately half of them were tagged. Total counts of kangaroos on the golf course were highest in summer, at the peak of the mating season, and lowest in winter, when many males but not females left the course. Almost all tagged adult females were sedentary, using only part of the golf course and adjacent native vegetation and residential blocks. In contrast, during the non-mating season (autumn and winter), many tagged adult males ranged widely across the town in a mix of native vegetation remnants, recreation reserves, vacant blocks, commercial properties and residential gardens. Annual fecundity of tagged females was generally high (≥70%), but survival of tagged juveniles was low (54%). We could not determine the cause of death of most juveniles. Vehicles were the major (47%) cause of mortality of tagged adults. Road-kills were concentrated (74%) in autumn and winter, and were heavily male biased: half of all tagged males died on roads compared with only 20% of tagged females. We predict that this novel and potent mortality factor will have profound, long-term impacts on the demography and behavior of the urban kangaroo population at Anglesea.

Recent studies suggest that roads can significantly impact bat populations. Though bats are one of the most threatened groups of European vertebrates, studies aiming to quantify bat mortality and determine the main factors driving it remain scarce. Between March 16 and October 31 of 2009, we surveyed road-killed bats daily along a 51-km-long transect that incorporates different types of roads in southern Portugal. We found 154 road-killed bats of 11 species. The two most common species in the study area, Pipistrellus kuhlii and P. pygmaeus , were also the most commonly identified road-kill, representing 72 % of the total specimens collected. About two-thirds of the total mortality occurred between mid July and late September, peaking in the second half of August. We also recorded casualties of threatened and rare species, including Miniopterus schreibersii , Rhinolophus ferrumequinum , R. hipposideros , Barbastella barbastellus , and Nyctalus leisleri . These species were found mostly in early autumn, corresponding to the mating and swarming periods. Landscape features were the most important variable subset for explaining bat casualties. Road stretches crossing or in the vicinity of high-quality habitats for bats—including dense Mediterranean woodland (“montado”) areas, water courses with riparian gallery, and water reservoirs—yielded a significantly higher number of casualties. Additionally, more road-killed bats were recorded on high-traffic road stretches with viaducts, in areas of higher bat activity and near known roosts.

Roads represent a threat to biodiversity, primarily through increased mortality from collisions with vehicles. Although estimating roadkill rates is an important first step, how roads affect long-term population persistence must also be assessed. We developed a trait-based model to predict roadkill rates for terrestrial bird and mammalian species in Europe and used a generalized population model to estimate their long-term vulnerability to road mortality. We found that ~194 million birds and ~29 million mammals may be killed each year on European roads. The species that were predicted to experience the highest mortality rates due to roads were not necessarily the same as those whose long-term persistence was most vulnerable to road mortality. When evaluating which species or areas could be most affected by road development projects, failure to consider how roadkill affects populations may result in misidentifying appropriate targets for mitigation.

1. The effectiveness of measures installed to mitigate wildlife road-kill depends on their placement along the road. Road-kill hotspots are frequently used to identify priority locations for mitigation measures. However, in situations where previous road mortality has reduced population size, road-kill hotspots may not indicate the best sites for mitigation. 2. The purpose of this study was to identify circumstances in which road-kill hotspots are not appropriate indicators for the selection of the best road-kill mitigation sites. We predicted that: (i) road-kill hotspots can move in time from high-traffic road segments to low-traffic segments, due to population depression near the high-traffic segment caused by road mortality; (ii) this shift will occur earlier for more mobile species because they should interact more often with the road; (iii) this shift can occur even if the low-traffic segment runs through lower quality habitat than the high-traffic segment. To test these predictions, we simulated population size and road-kill over time for two populations, one exposed to a road segment with high traffic and the other to a road segment with low traffic. 3. Our simulation results supported Predictions 1 and 3, while Prediction 2 was not supported. 4. Synthesis and applications. Our results indicate that, for new roads, road-kill hotspots can be useful to indicate appropriate sites for mitigation. On older roads, road-kill hotspots may not indicate the best sites for road mitigation due to population depression caused by road mortality. Direct measures of the road impact on the population, such as per capita mortality, are better indicators of appropriate mitigation sites than road-kill hotspots.