Daily Activity Rhythms of Animals in the Southwest Mountains, China: Influences of Interspecific Relationships and Seasons

Temporal and spatial factors regulate the interactions between apex predators, mesocarnivores, and herbivores. Prey adjust their activity patterns and spatial utilization based on predator activities; in turn, predators also adapt to the activities of their prey. To elucidate the factors influencing the daily activity rhythms of animals, 115 camera traps were established from September 2019 to June 2023 to assess the influences of interspecific relationships and seasons on the daily activity rhythms of animals in the southwest mountains of China. The species captured by the cameras included six Carnivora (such as Panthera pardus and Lynx lynx), six Artiodactyla (such as Moschus spp. and Rusa unicolor), one Primate (Macaca mulatta), and two Galliformes (Crossoptilon crossoptilon, Ithaginis cruentus). The results demonstrated that the 15 species exhibited different activity rhythms and peak activities to reduce intense resource competition. There were differences in the species' activity rhythms in different seasons, with competition among different species being more intense in the cold season than in the warm season. In predation relationships, the overlap coefficient in the cold season exceeded that of the warm season, possibly due to the abundant resources in summer and food scarcity in winter. In competitive relationships, 15 pairs of species exhibited significantly higher overlap coefficients in the cold season compared to the warm season, possibly due to increased demands for energy during the cold period or seasonal changes in predatory behavior. By analyzing the daily and seasonal activity patterns of dominant species in the study area, temporal niche overlaps were established to compare the competition levels between species. These findings indicate that the activity rhythms of the animals in this area not only result from evolutionary adaptation but are also influenced by season, food resources, and interspecific relationships (predation and competition). Thus, efforts should be made to reduce human interference, protect food resources in the winter, and monitor animals' interspecific relationships to protect animal diversity and maintain the stability of the ecosystem in this biodiversity hotspot in China.

Influence of biotic and abiotic factors on home range size and shape of invasive wild pigs (Sus scrofa)

BACKGROUND

Determining factors influencing animal movements at a temporal scale that is similar to that at which management actions are conducted (e.g. weekly) is crucial for identifying efficient methods of wildlife conservation and management. Using global positioning system (GPS) data from 49 wild pigs in the southeast United States, we constructed weekly 50% and 95% utilization distributions to quantify the effects of biotic and abiotic factors on weekly core area and home range size, as well as home range shape.

RESULTS

We found vegetative composition (i.e. proportion of bottomland hardwoods), season (based on forage availability), meteorological conditions (i.e. temperature and pressure), and sex influenced wild pig weekly home range and core area size, while vegetative composition (i.e. proportion of upland pines) and landscape features (i.e. distance to streams) also were important factors influencing home range shape. At close distances to streams, wild pigs had more elongate home ranges when their home ranges comprised less upland pine habitat; however, farther from streams, there was no change in home range shape across fluctuating proportions of upland pines.

CONCLUSION

These results demonstrate that fine-scale wild pig home ranges and movements are pliable from week to week and influenced by several habitat, landscape, and meteorological attributes that can easily be quantified from available land use and meteorological databases. These findings are important for designing monitoring studies, identifying high risk zones for disease transmission, planning response to disease emergence events, and allowing more effective and efficient short-term management planning.

Spatiotemporal Analysis of Road Crashes with Animals in Poland

This article describes the issue of the influences of the time factor and wildlife populations on road animal–vehicle crashes. The article presents problems associated with animal–vehicle crashes in Poland. They are the subject of many court disputes. For the purposes of the study, data on animal–vehicle road crashes were obtained from the SEWiK database, as well as data on the numbers of animals in hunting districts from 2016–2020. The relationship between the number of road crashes and time was analysed, and the relationships between the numbers of road crashes and the animal populations, as well as the locations, types, and categories of the hunting districts, were analysed as well. The factors related to changes in the wildlife populations, road crashes in previous years, and the lengths of the road networks were also analysed. The research shows no relationship between the abundance of a particular species and the number of road crashes. Instead, there is a correlation between the number of crashes in previous years and the road network length.

The effectiveness of decommissioning roadside mineral licks on reducing moose (Alces alces) activity near highways: implications for moose–vehicle collisions

Roadside mineral licks form when road salt used to de-ice highways in winter runs off road surfaces and accumulates in roadside ditches. Some ungulates are attracted to these roadside licks as they seek to satisfy their mineral requirements. Within the distribution of moose (Alces alces (Linnaeus, 1758)) in North America, motorists often encounter moose visiting roadside licks in mid-summer, with many jurisdictions reporting summer peaks in moose–vehicle collisions (MVCs) at these locations. We used camera traps to monitor the moose visitation of 22 roadside locations (including roadside licks, roadside ponds, and dry roadsides) in central British Columbia, Canada, from December 2009 to July 2020. We tested the efficacy of treatment (decommissioning) methods used to reduce moose visitation to roadside licks and roughly estimated decommissioning costs. Moose visitation to roadside licks was greatest from May to July. As we hypothesized, untreated licks were visited more often by moose than decommissioned licks, roadside ponds (absence of road salt), and dry roadsides. Decommissioning roadside licks by replacing or mixing lick waters and soils with materials, such as riprap, cedar mulch, pine logs, or dog (Canis lupus familiaris Linnaeus, 1758) fur and human (Homo sapiens Linnaeus, 1758) hair, is an effective and inexpensive means of reducing moose visitations to roadside areas and should increase motorist safety where roadside licks are visited by moose.

Effect of Daylight Saving Time clock shifts on white-tailed deer-vehicle collision rates

To devise effective measures for reducing hazardous wildlife-vehicle collisions, it is necessary to know when during the year accidents occur most frequently, and what factors cause the seasonal patterns. Daylight Saving Time (DST) 1-h clock-shifts around the spring and fall equinoxes at temperate zone latitudes are associated with increased vehicle accidents, attributed to driver error caused by disrupted sleep patterns and changes in visibility during peak driving times. Collision with deer is a significant cause of motor vehicle accidents in North America; in New York State alone, 65,000 vehicle accidents annually are caused by collision with white-tailed deer (Odocoileus virginianus). We asked whether white-tailed deer-vehicle collisions (DVC) increased in frequency after DST clock shifts in New York State, by analyzing 35,167 New York State DVC reports from 2005 to 2007. For the spring, when the clock is shifted an hour forward relative to sunrise (i.e. later sunrise and sunset), there was either no change or possibly a small decrease in workweek evening DVC after the clock shift. For fall, when the clock is shifted an hour back relative to sunrise (i.e. earlier sunrise and sunset), the DVC rate was far higher than spring. The DVC rate was higher after the clock shift than before, caused in part by an ongoing seasonal trend for increasing DVC associated with deer behavior around the time of rut, peaking about two weeks after the clock shift. However, there was also a reduction in workweek morning DVC after clock-shift, but an even greater increase in DVC in the evening. DVC rates are highest around dusk and during the fall, and the fall DST clock-shift caused more workweek commuter traffic to coincide with the annual hourly period of peak risk of DVC. We conclude that in New York State, DST clock-shift results in an increase in the number of DVC, and therefore injuries and property damage associated with such accidents. The justification for DST clock-shifts is controversial; when evaluating the benefits and costs, one should include the consequences for risk of wildlife-vehicle collisions, especially in regions where ungulate-vehicle accidents are frequent, and clock-shifts coincide with the rut or other periods of peak accident risk.

The declining occurrence of moose (Alces alces) at the southernmost edge of its range raise conservation concerns

The border region between Austria, the Czech Republic, and Germany harbors the most south-western occurrence of moose in continental Europe. The population originated in Poland, where moose survived, immigrated from former Soviet Union or were reintroduced after the Second World War expanded west- and southwards. In recent years, the distribution of the nonetheless small Central European population seems to have declined, necessitating an evaluation of its current status. In this study, existing datasets of moose observations from 1958 to 2019 collected in the three countries were combined to create a database totaling 771 records (observations and deaths). The database was then used to analyze the following: (a) changes in moose distribution, (b) the most important mortality factors, and (c) the availability of suitable habitat as determined using a maximum entropy approach. The results showed a progressive increase in the number of moose observations after 1958, with peaks in the 1990s and around 2010, followed by a relatively steep drop after 2013. Mortality within the moose population was mostly due to human interactions, including 13 deadly wildlife-vehicle collisions, particularly on minor roads, and four animals that were either legally culled or poached. Our habitat model suggested that higher altitudes (ca. 700-1,000 m a.s.l.), especially those offering wetlands, broad-leaved forests and natural grasslands, are the preferred habitats of moose whereas steep slopes and areas of human activity are avoided. The habitat model also revealed the availability of large core areas of suitable habitat beyond the current distribution, suggesting that habitat was not the limiting factor explaining the moose distribution in the study area. Our findings call for immediate transboundary conservation measures to sustain the moose population, such as those aimed at preventing wildlife-vehicle collisions and illegal killings. Infrastructure planning and development activities must take into account the habitat requirements of moose.

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.

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.

Habitat suitability model with maximum entropy approach for European roe deer (Capreolus capreolus) in the Black Sea Region

Evaluating the relationships between wildlife species and their habitats helps to predict effects of habitat change for present and future management of wild animal populations. Building ecological models are good ways to understand and manage wildlife populations and to predict various environmental scenarios. Recently, management of ungulates is becoming more important in Europe due to a high demand of hunting and their role in biodiversity. European roe deer (Capreolus capreolus) is the smallest species of cervids and has a widespread distribution in Turkey. In this study, two habitat suitability models of roe deers, living in the Black Sea Region in Turkey, were created by using a maximum entropy (MaxEnt) approach. Two wildlife development areas, which have widely different habitat types, were selected as study sites. As a result of this study, area under the curve (AUC) values were found to be above 0.80. According to the modeling results, in two different habitat types, ecological variables are quite similar in general. This study is the first study on modeling European roe deers in Turkey.

Incorporating Road Crossing Data into Vehicle Collision Risk Models for Moose (Alces americanus) in Massachusetts, USA

Wildlife-vehicle collisions are a human safety issue and may negatively impact wildlife populations. Most wildlife-vehicle collision studies predict high-risk road segments using only collision data. However, these data lack biologically relevant information such as wildlife population densities and successful road-crossing locations. We overcome this shortcoming with a new method that combines successful road crossings with vehicle collision data, to identify road segments that have both high biological relevance and high risk. We used moose (Alces americanus) road-crossing locations from 20 moose collared with Global Positioning Systems as well as moose-vehicle collision (MVC) data in the state of Massachusetts, USA, to create multi-scale resource selection functions. We predicted the probability of moose road crossings and MVCs across the road network and combined these surfaces to identify road segments that met the dual criteria of having high biological relevance and high risk for MVCs. These road segments occurred mostly on larger roadways in natural areas and were surrounded by forests, wetlands, and a heterogenous mix of land cover types. We found MVCs resulted in the mortality of 3% of the moose population in Massachusetts annually. Although there have been only three human fatalities related to MVCs in Massachusetts since 2003, the human fatality rate was one of the highest reported in the literature. The rate of MVCs relative to the size of the moose population and the risk to human safety suggest a need for road mitigation measures, such as fencing, animal detection systems, and large mammal-crossing structures on roadways in Massachusetts.

Dash Cam videos on YouTube™ offer insights into factors related to moose-vehicle collisions

To gain a better understanding of the dynamics of moose-vehicle collisions, we analyzed 96 videos of moose-vehicle interactions recorded by vehicle dash-mounted cameras (Dash Cams) that had been posted to the video-sharing website YouTube™. Our objective was to determine the effects of road conditions, season and weather, moose behavior, and driver response to actual collisions compared to near misses when the collision was avoided. We identified 11 variables that were consistently observable in each video and that we hypothesized would help to explain a collision or near miss. The most parsimonious logistic regression model contained variables for number of moose, sight time, vehicle slows, and vehicle swerves (AIC_cw = 0.529). This model had good predictive accuracy (AUC = 0.860, SE = 0.041). The only statistically significant variable from this model that explained the difference between moose-vehicle collisions and near misses was 'Vehicle slows'. Our results provide no evidence that road surface conditions (dry, wet, ice or snow), roadside habitat type (forested or cleared), the extent to which roadside vegetation was cleared, natural light conditions (overcast, clear, twilight, dark), season (winter, spring and summer, fall), the presence of oncoming traffic, or the direction from which the moose entered the roadway had any influence on whether a motorist collided with a moose. Dash Cam videos posted to YouTube™ provide a unique source of data for road safety planners trying to understand what happens in the moments just before a moose-vehicle collision and how those factors may differ from moose-vehicle encounters that do not result in a collision.

Quantifying drivers of wild pig movement across multiple spatial and temporal scales

BACKGROUND

The movement behavior of an animal is determined by extrinsic and intrinsic factors that operate at multiple spatio-temporal scales, yet much of our knowledge of animal movement comes from studies that examine only one or two scales concurrently. Understanding the drivers of animal movement across multiple scales is crucial for understanding the fundamentals of movement ecology, predicting changes in distribution, describing disease dynamics, and identifying efficient methods of wildlife conservation and management.

METHODS

We obtained over 400,000 GPS locations of wild pigs from 13 different studies spanning six states in southern U.S.A., and quantified movement rates and home range size within a single analytical framework. We used a generalized additive mixed model framework to quantify the effects of five broad predictor categories on movement: individual-level attributes, geographic factors, landscape attributes, meteorological conditions, and temporal variables. We examined effects of predictors across three temporal scales: daily, monthly, and using all data during the study period. We considered both local environmental factors such as daily weather data and distance to various resources on the landscape, as well as factors acting at a broader spatial scale such as ecoregion and season.

RESULTS

We found meteorological variables (temperature and pressure), landscape features (distance to water sources), a broad-scale geographic factor (ecoregion), and individual-level characteristics (sex-age class), drove wild pig movement across all scales, but both the magnitude and shape of covariate relationships to movement differed across temporal scales.

CONCLUSIONS

The analytical framework we present can be used to assess movement patterns arising from multiple data sources for a range of species while accounting for spatio-temporal correlations. Our analyses show the magnitude by which reaction norms can change based on the temporal scale of response data, illustrating the importance of appropriately defining temporal scales of both the movement response and covariates depending on the intended implications of research (e.g., predicting effects of movement due to climate change versus planning local-scale management). We argue that consideration of multiple spatial scales within the same framework (rather than comparing across separate studies post-hoc) gives a more accurate quantification of cross-scale spatial effects by appropriately accounting for error correlation.

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.

Patterns and Composition of Road-Killed Wildlife in Northwest Argentina

Roads have important effects on wildlife, such as natural habitat fragmentation and degradation and direct killing of fauna, which leads to reductions in wildlife population size. We focused on a principal road in Northwest Argentina to test for the effect of seasonality and landscape features on the composition of road-killed wildlife. We conducted regularly scheduled road trips during the dry and wet seasons. We recorded the presence or absence of a vegetation curtain or hedge along the road. We measured land use by remote sensing in a 500 m buffer along the road. We compared the abundance of animals killed between seasons (dry and wet) for different taxonomic groups (mammals, birds and reptiles) and for different origins (domestic and native). We built linear mixed models to test the effect of landscape features on the abundance of killed animals. Two hundred and ninety-three individuals were killed, belonging to 35 species; 75.8 % were native and 24.2 % domestic species. The majority of animals killed were mid-sized mammals. More animals were killed during the dry season. The most important factors to explain the wildlife road-killing were the season and the proportion of agricultural landscape. The composition of the killed animals changed with the season. The proportion of agricultural landscape incremented the number of killed birds and mammals during both seasons, without affecting reptiles. The ratio of wild to domestic animals killed was dependent on the season. This study sets a precedent as the first in road ecology in Northwest Argentina and should be taken into account for road planning and regulation.

Behavioral traits and airport type affect mammal incidents with U.S. civil aircraft

Wildlife incidents with aircraft cost the United States (U.S.) civil aviation industry >US$1.4 billion in estimated damages and loss of revenue from 1990 to 2009. Although terrestrial mammals represented only 2.3 % of wildlife incidents, damage to aircraft occurred in 59 % of mammal incidents. We examined mammal incidents (excluding bats) at all airports in the Federal Aviation Administration (FAA) National Wildlife Strike Database from 1990 to 2010 to characterize these incidents by airport type: Part-139 certified (certificated) and general aviation (GA). We also calculated relative hazard scores for species most frequently involved in incidents. We found certificated airports had more than twice as many incidents as GA airports. Incidents were most frequent in October (n = 215 of 1,764 total) at certificated airports and November (n = 111 of 741 total) at GA airports. Most (63.2 %) incidents at all airports (n = 1,523) occurred at night but the greatest incident rate occurred at dusk (177.3 incidents/hr). More incidents with damage (n = 1,594) occurred at GA airports (38.6 %) than certificated airports (19.0 %). Artiodactyla (even-toed ungulates) incidents incurred greatest (92.4 %) damage costs (n = 326; US$51.8 million) overall and mule deer (Odocoileus hemionus) was the most hazardous species. Overall, relative hazard score increased with increasing log body mass. Frequency of incidents was influenced by species relative seasonal abundance and behavior. We recommend airport wildlife officials evaluate the risks mammal species pose to aircraft based on the hazard information we provide and consider prioritizing management strategies that emphasize reducing their occurrence on airport property.

A review on the temporal pattern of deer-vehicle accidents: impact of seasonal, diurnal and lunar effects in cervids

The increasing number of deer-vehicle-accidents (DVAs) and the resulting economic costs have promoted numerous studies on behavioural and environmental factors which may contribute to the quantity, spatiotemporal distribution and characteristics of DVAs. Contrary to the spatial pattern of DVAs, data of their temporal pattern is scarce and difficult to obtain because of insufficient accuracy in available datasets, missing standardization in data aquisition, legal terms and low reporting rates to authorities. Literature of deer-traffic collisions on roads and railways is reviewed to examine current understanding of DVA temporal trends. Seasonal, diurnal and lunar peak accident periods are identified for deer, although seasonal pattern are not consistent among and within species or regions and data on effects of lunar cycles on DVAs is almost non-existent. Cluster analysis of seasonal DVA data shows nine distinct clusters of different seasonal DVA pattern for cervid species within the reviewed literature. Studies analyzing the relationship between time-related traffic predictors and DVAs yield mixed results. Despite the seasonal dissimilarity, diurnal DVA pattern are comparatively constant in deer, resulting in pronounced DVA peaks during the hours of dusk and dawn frequently described as bimodal crepuscular pattern. Behavioural aspects in activity seem to have the highest impact in DVAs temporal trends. Differences and variations are related to habitat-, climatic- and traffic characteristics as well as effects of predation, hunting and disturbance. Knowledge of detailed temporal DVA pattern is essential for prevention management as well as for the application and evaluation of mitigation measures.

Characterizing Moose–Vehicle Collision Hotspots in Northern British Columbia

To have a better understanding of the ecological factors that may contribute to moose Alces alces and vehicle collisions in northern British Columbia, we analyzed Wildlife Accident Reporting System data that were collected between 2000 and 2005 by highway maintenance contractors. We delineated 29 moose-vehicle collision hotspots and 15 control sites at which we assessed environmental and road infrastructure attributes through field surveys and remotely sensed data. A logistic regression model including both coarse- and fine-scale environmental factors suggested that hotspots were more likely to be characterized by the number of roadside mineral licks and bisection of the highway corridor through black spruce forest–sphagnum bog habitat and swamps. The absence of rivers within 1 km and less lake area within 500 m of the highway also better characterized hotspots than controls. At the fine scale, deciduous forest cover along the highway edge and the proportion of browse to nonbrowse vegetation between the road shoulder and forest edge were also related to collision sites. Based on these data, the mitigation of collision hotspots should include decommissioning roadside mineral licks where they occur and cutting roadside brush to improve driver visibility and reduce browse resprouting and attractiveness. Where new road construction or road realignments are being contemplated, we recommend considering routes with more lake area, more rivers, fewer swamps, and fewer black spruce forest–sphagnum bog habitats to help reduce collisions. We discuss the utility of installing novel warning signage in areas where collisions are recurrent.

Spatial wildlife-vehicle collision models: a review of current work and its application to transportation mitigation projects

In addition to posing a serious risk to motorist safety, vehicle collisions with wildlife are a significant threat for many species. Previous spatial modeling has concluded that wildlife-vehicle collisions (WVCs) exhibit clustering on roads, which is attributed to specific landscape and road-related factors. We reviewed twenty-four published manuscripts that used generalized linear models to statistically determine the influence that numerous explanatory predictors have on the location of WVCs. Our motivation was to summarize empirical WVC findings to facilitate application of this knowledge to planning, and design of mitigation strategies on roads. In addition, commonalities between studies were discussed and recommendations for future model design were made. We summarized the type and measurement of each significant predictor and whether they potentially increased or decreased the occurrence of collisions with ungulates, carnivores, small-medium vertebrates, birds, and amphibians and reptiles. WVCs commonly occurred when roads bisect favorable cover, foraging, or breeding habitat for specific species or groups of species. WVCs were generally highest on road sections with high traffic volumes, or low motorist visibility, and when roads cut through drainage movement corridors, or level terrain. Ungulates, birds, small-medium vertebrates, and carnivore collision locations were associated with road-side vegetation and other features such as salt pools. In several cases, results were spurious due to confounding and interacting predictors within the same model. For example, WVCs were less likely to occur when a road bisected steep slopes; however, steep slopes may be located along specific road-types and habitat that also influence the occurrence of WVCs. In conclusion, this review showed that much of the current literature has gleaned the obvious, broad-scale relationships between WVCs and predictors from available data sets, and localized studies can provide unique and novel results. Future research requires specific modeling for each target species on a road-by-road basis, and measuring the predictive power of model results within similar landscapes. In addition, research that builds on the current literature by investigating rare anomalies and interacting variables will assist in providing sound comprehensive guidelines for wildlife mitigation planning on roads.

A comparison of data sets varying in spatial accuracy used to predict the occurrence of wildlife-vehicle collisions

Wildlife-vehicle collisions (WVCs) pose a significant safety and conservation concern in areas where high-traffic roads are situated adjacent to wildlife habitat. Improving transportation safety, accurately planning highway mitigation, and identifying key habitat linkage areas may all depend on the quality of WVC data collection. Two common approaches to describe the location of WVCs are spatially accurate data derived from global positioning systems (GPS) or vehicle odometer measurements and less accurate road-marker data derived from reference points (e.g., mile-markers or landmarks) along the roadside. In addition, there are two common variable types used to predict WVC locations: (1) field-derived, site-specific measurements and (2) geographic information system (GIS)-derived information. It is unclear whether these different approaches produce similar results when attempting to identify and explain the location of WVCs. Our first objective was to determine and compare the spatial error found in road-marker data (in our case the closest mile-marker) and landmark-referenced data. Our second objective was to evaluate the performance of models explaining high- and low-probability WVC locations, using congruent, spatially accurate (<3-m) and road-marker (<800-m) response variables in combination with field- and GIS-derived explanatory variables. Our WVC data sets were comprised of ungulate collisions and were located along five major roads in the central Canadian Rocky Mountains. We found that spatial error (mean +/- SD) was higher for WVC data referenced to nearby landmarks (516 +/- 808 m) than for data referenced to the closest mile-marker data (401 +/- 219 m). The top-performing model using the spatially accurate WVC locations contained all explanatory variable types, whereas GIS-derived variables were only influential in the best road-marker model and the spatially accurate reduced model. Our study showed that spatial error and sample size, using road-marker data for ungulate species, are important to consider for model output interpretation, which will impact the appropriate scale on which to apply modeling results. Using road-marker references <1.6 km or GPS-derived data locations may represent an optimal compromise between data acquisition costs and analytical performance.