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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.

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.

1. Vehicle collisions with wild animals are a serious problem that justifies the wide-spread application of mitigation measures such as road fencing and provision of crossing structures. Models that predict the best location for mitigation measures can improve wildlife survival and road safety. 2. A database of 2067 records of animal-vehicle collisions was used to create two data sets at different spatial scales. The first comprised records of road sections of 1 km length with high rates of collision in combination with road sections with a low number of collisions. The second comprised records of collision and no collision incidence at points on the road system at a 0·1-km scale. Logistic regression was used to investigate the relationship between incidence of collision and measured habitat features in each data set. The models were validated with a subset of the original data not used in developing the models. 3. Road sections with high collision rates were associated with areas having high forest cover, low crop cover, low numbers of buildings and high habitat diversity. The fitted model achieved a significant predictive success during validation (χ2= 4·82, 1 d.f., P = 0·028), with more than 70% correct classification of cases. 4. Specific collision points typically had no guard-rails or lateral embankments, were not near underpasses, crossroads or buildings, and featured hedges or woodland near the road. The fitted model also showed a significant predictive power in validation (64% correct classification, χ2= 9·51, 1 d.f., P = 0.002) and accurately predicted 85·1% of collision points. 5. Synthesis and applications. Predictive models of animal-vehicle collision locations should be used at both a landscape level and a local scale during the process of road design and implementation of mitigation measures. Modelling of collision risk could inform decisions on road alignment and on the exact location of crossing structures for mammals, to improve wildlife survival and road safety. This is the first study integrating both landscape and local scales of analysis for the variables associated with animal-vehicle collisions.

To highlight the benefit of using habitat use to improve the accuracy of predictive road fatality models. The Snowy Mountains Highway in southern New South Wales, Australia. A binary logistic regression model was constructed using wombat fatality presences and randomly generated absences. Species-specific habitat variables were included as predictors in the model selection process as well as two spatially explicit measures of wombat habitat use. Generalized additive models (GAMs) were constructed for each possible combination of predictors in R. The final model was selected by comparing all models subsets for the eight predictors and employing the one standard error rule to select the best model set. The final predictive model had high discriminatory power and incorporated both measures of species habitat use, greatly exceeding the variation explained by a previously published model for the same species and road. Our findings highlight the importance of incorporating variables that describe habitat use by fauna for predictive modelling of animal-vehicle crashes. Reliance upon models that ignore landscape patterns are limited in their capacity to identify hotspots and inform managers of locations to engage in mitigation.

Abundance of mule deer (Odocoileus hemionus) in western North America is often considered lower than desirable for hunting. Some coastal populations of Columbian black-tailed deer (O. h. columbianus) in California, USA, near urban development, however, are perceived as a nuisance and may be overabundant. To determine the density of a potential nuisance population in Marin County, California, we used a combination of fecal DNA surveys, camera stations, and 2 sources of ancillary data on wildlife observations. We estimated an average density of 18.3 deer/km² (90% CI=15.8–20.7) throughout Marin County during late summer and early fall, 2015 and 2016. Within the county, areas with intermediate human density (885 people/km², 90% CI=125–1,646) were associated with the highest deer densities (25–44/km²). Our estimate of average deer density was 1.7–6.1 times higher than published density estimates for deer from elsewhere in California and on the low end of densities reported for mule and white-tailed (O. virginianus) deer in regions where they routinely cause a nuisance to humans. High black-tailed deer densities in Marin County may be partially attributed to a paucity of large predators, but more investigation is warranted to evaluate the effects of a recent increase in coyotes (Canis latrans) on the deer population. Analyses of highway road kill rates and citizen science surveys suggest that the deer population in Marin County has been stable over the past 10 years. Our results demonstrate how robust estimation of deer density can inform human–wildlife conflict issues, not just managed hunting.

The expansion of road networks and increased traffic loads have resulted in an increase in the problem of wildlife roadkill, which has a serious impact on both human safety and the wildlife population. However, roadkill data are collected primarily from the incidental sighting, thus they often lack the true-absence information. This study aims to identify the factors associated with Korean water deer (Hydropotes inermis) roadkill in Korea using the point processing modeling (PPM) approach. Water deer roadkill point data were fitted with explanatory variables derived from forest cover type, topography, and human demography maps and an animal distribution survey. Water deer roadkill showed positive associations with road density, human population density, road width, and water deer detection point density. Slope and elevation showed negative associations with roadkill. The traffic volume and adjacent water deer population may be the major driving factors in roadkill events. The results also imply that the PPM can be a flexible tool for developing roadkill mitigation strategy, providing analytical advantages of roadkill data, such as clarification of model specification and interpretation, while avoiding issues derived from a lack of true-absence information.

ContextIdentifying core migratory pathways and associated threats is important for developing conservation priorities for declining migratory species, such as eastern monarch butterflies (Danaus plexippus L.).ObjectivesCharacterization of monarch fall migration core pathways and annual variability was compared using kernel density estimation models (KDEMs) and MaxEnt ecological niche models. Potential anthropogenic hazards were identified across migratory pathways and related to conservation strategies.MethodsJourney North citizen scientist monarch overnight roost data from 2002 to 2016 were used to model the fall migration at 10 km spatial resolution with MaxEnt and KDEMs. Potential anthropogenic threats to the fall migration were spatially identified along core migratory routes.ResultsThe KDEM migratory pathways best represented patterns of monarch movement towards overwintering locations. Migratory routes varied as much as 200 km from east to west in the southern Central Flyway, which was also the only area identified with monarch roadkill hotspots. Potential threats from mosquito adulticide ultra-low volume (ULV) spraying were concentrated along Eastern Flyway coastal areas. Potential nectar resource loss or contamination from high usage of glyphosate herbicide and neonicotinoid insecticides was greatest in the Midwest, within the core route of the Central Flyway.ConclusionsMaxEnt and KDEM were complementary in modeling monarch migratory pathways. Monarch roadkill estimation and mitigation strategies are most needed in the southern core migratory pathways through Texas and Mexico. High quality nectar resource enhancement could help to mitigate potential threats from mosquito ULV spraying and nectar resource loss or contamination in coastal areas and the Midwest, respectively.

Road networks are the main source of mortality for many species. Amphibians, which are in global decline, are the most road-killed fauna group, due to their activity patterns and preferred habitats. Many different methodologies have been applied in modeling the relationship between environment and road-kills events, such as logistic regression. Here, we compared the performance of five regression techniques to relate amphibians’ road-kill frequency to environmental variables. For this, we surveyed three country roads in northern Portugal in search of road-killed amphibians. To explain the presence of road-kills, we selected a set of environmental variables important for the presence of amphibians and the occurrence of road-kills. We compared the performances of five modeling techniques: (i) generalized linear models, (ii) generalized additive models, (iii) random forest, (iv) boosted regression trees, and (v) geographically weighted regression. The boosted regression trees and geographically weighted regression techniques performed the best, with a percentage of deviance explained between 61.8% and 76.6% and between 55.3% and 66.7%, respectively. Moreover, the geographically weighted regression showed a great advantage over the other techniques, as it allows mapping local parameter coefficients as well as local model performance (pseudo-R2). The results suggest that geographically weighted regression is a useful tool for road-kill modeling, as well as to better visualize and map the spatial variability of the models.

Roadkill is of ecological importance so that there is increasing academic research to understand the causes and patterns of roadkills and their impact on ecosystems. This work is motivated by the study on roadkills of endangered Bufo calamita ( B. calamita ) (The natterjack toad) out of amphibian roadkills. The status of B. calamita is regarded as unfavorable due to large population declines. In the mentioned study, B. calamita and total amphibian roadkills were recorded via distance sampling on a National Road of Southern Portugal between March 1995 and March 1997. The traditional binomial modeling of these data are challenged by three issues. First, the zeros in B. calamita counts far exceeded its nominal level. Second, there is likely serial correlation among observations along the road. Finally, there is varying number of total amphibian roadkills at each sampling location; therefore, there is likely randomness in the number of total amphibian roadkills. All these features may contribute to overdispersion in the binomial observations. These three issues are routinely addressed one at a time separately, the first through zero-inflated binomial models, the second, for example, by means of random effects models for serially correlated binomial data and the third by models for binomial data with random cluster sizes. Therefore the data cannot be adequately modeled by any of these separate models. In this paper, we propose a new model to tackle these three issues simultaneously in the binomial analysis of B. calamita roadkills out of amphibian roadkills. Our approach is generally applicable to other binomial data with these three features.