Current Landscape Ecology Reports

, Volume 2, Issue 1, pp 1–11 | Cite as

Effects of Road Density and Pattern on the Conservation of Species and Biodiversity

Effects of Landscape Structure on Conservation of Species and Biodiversity (M Betts, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Effects of Landscape Structure on Conservation of Species and Biodiversity

Abstract

The development and presence of roads can reduce landscape permeability, lead to habitat loss, and increase habitat fragmentation. It is these fundamental changes in landscape structure that can have both direct and indirect impacts on the conservation of species and biodiversity. In this review, I examine 215 research studies conducted between 2011 and 2015 that explore the impacts of roads and road networks on a wide range of species. I divided these studies into four main categories: 1) the direct effects of roads on wildlife, 2) the indirect effects of roads on wildlife, 3) the consequences of road networks on wildlife populations, and 4) survey design and mitigation including both innovations and evaluations. I found that the majority of studies (38%) explored the indirect effects of roads on wildlife, including displacement, fitness consequences, and road crossing ability of wildlife. Nevertheless, despite there being a pressing need to understand how existing road networks impact wildlife and how increasing road density may influence local and regional population persistence, only 10% of the studies considered the implications of road networks on wildlife. However, there is an increasing trend towards the development of predictive models that can be used for a better understanding of road network impacts, assess landscape connectivity, and devise mitigation. This review also highlighted the continued need to devise and evaluate mitigation measures so transportation authorities and conservation practitioners may be better equipped to address the ecological implications of roads and proposed road development.

Keywords

Barrier effects Habitat fragmentation Landscape permeability Road mitigation Road networks Wildlife-vehicle collisions 

Introduction

For three decades now the field of road ecology has brought to light the impacts that roads and road networks have on the landscape, its wildlife, and subsequent ecosystem stability [1, 2]. This paper presents seminal reviews from associating the presence of roads with the direct mortality of wildlife, hindering wildlife movement both physically and behaviorally, and the loss and degradation of habitat, all of which can have far-reaching implications for regional population dynamics, species diversity, and ecosystem function [3, 4, 5]. To date, very few landscapes remain devoid of roads, and as human populations continue to increase it is likely that road density will increase as well, whether it be by new developments, road improvements, or road expansion schemes. There is, therefore, a pressing need to employ what we have learnt and use it to identify the species and habitats that are most vulnerable to the negative impacts of roads, consolidate the different mitigation strategies we have tried and tested, and establish where our knowledge is still lacking in order to recommend further research. In this quantitative review, I attempt to address these priorities.

Methods

This review is based on peer-reviewed publications obtained from an ISI Web of Knowledge database search of all articles from 2011 to 2015, which present research on the effects of roads and traffic on wildlife. My search was not limited to location or taxa. I used “roads”, “traffic”, “transportation infrastructure”, “vehicle”, and “anthropogenic disturbance” as key search words, along with “wildlife” or “animal”.

For this review, I divided all research found into four main categories, which considered 1) the direct effects of roads on wildlife, 2) the indirect effects of roads on wildlife, 3) the consequences of road networks on wildlife populations, and 4) survey design and mitigation including both innovations and evaluations. These categories are discussed in more detail below. However, to summarize the former two categories: the direct and indirect impacts of roads on wildlife include studies that focus on the effect of a single road or a select number of roads (for comparison) on wildlife individuals, such as wildlife-vehicle collisions, avoidance, and attraction. Note that they do not explicitly study the effects of roads on population persistence. Instead, studies considered in the third category, the consequences of road networks on wildlife populations, do address the larger scale impacts of roads (e.g., within a landscape) on population persistence. The final category, survey design and mitigation, considers studies that present innovative ideas, and/or evaluate mitigation intended to reduce the impacts of roads on wildlife.

Results

I found a total of 215 studies presenting research on the effects of roads and road networks on wildlife (see Appendix A for the full list of literature). These studies included animals from a wide range of taxa, with 86 studies focusing specifically on mammals (39%), 36 on birds (16%), 23 on reptiles (11%), 17 on amphibians (8%), five on invertebrates (2%), and three on fish (1%). The remaining studies considered multiple species across a variety of taxonomic groups (comprising 23%). A total of 57 studies documented research on species of conservation concern (26%), and almost half of these studies included species listed as threatened or endangered (26 of 57).

The majority of studies were conducted in North America (46%) and Europe (30%), but there were studies from all over the world, including Australia, Africa, Asia, and South America (Fig. 1). Moreover, 24% of studies were undertaken in protected areas (such as Biosphere Reserves, national parks, wildlife refuges, and nature reserves; Fig. 1) and a further nine studies involved a controlled experiment in a laboratory or field setting.
Fig. 1

Number of road-related studies conducted in each continent from 2011 to 2015. The cross-hatched sections of each bar represent the number of studies conducted in protected areas

Of the four categories listed, a total of 55 studies specifically explored the direct impacts of roads on wildlife, 73 investigated the indirect impacts, nine studies considered both direct and indirect impacts, 15 explored the consequences of road networks on wildlife populations, 58 focused on survey design and mitigation, and finally five studies tested and recommended new techniques to explore the consequences of road networks on wildlife (i.e., a combination of categories 3 and 4).

Direct Impacts of Roads on Wildlife

The direct mortality of wildlife through wildlife-vehicle collisions has been the most widely acknowledged impact of roads over the last three decades, not only because such fatalities are noticeable to the general public, but because these types of wildlife encounters can affect public safety, particularly when large herbivores and carnivores are involved. Of the 64 studies that explored direct mortality, 25% were conducted to quantify road-related mortality in large mammals (16 of 64), predominately moose and other large deer species [6, 7, 8]. While wildlife-vehicle collisions with large mammals continue to be reported, there appears to be a shift in research towards devising solutions and improving existing mitigation for such species (see Survey Design and Mitigation section). Furthermore, many research studies were focused on identifying and quantifying road-related mortality in species whose population persistence could be affected. For example, of the 27 studies that investigated road-related mortality in up to three related species, 15 studies involved species of conservation concern, while the remaining 12 studies considered the common and widespread species that are often reported in wildlife-vehicle collisions. Amphibians appeared to generate the most concern, with a number of multiple-taxa studies finding that they made up the highest percentage of roadkills (as much as 80% recorded, [9, 10, 11, 12]). Studies warn that with many amphibian populations already declining globally any additional non-natural mortality could further impact population persistence [13]. The reason as to why amphibians in general appear to be more vulnerable to wildlife-vehicle collisions than other taxa has been related to their ecology and life history. Three studies confirmed that road mortality of amphibians peaked as they attempted to cross roads during their migration in the spring from terrestrial hibernacula to aquatic breeding habitats [11, 12, 14]. These studies recommended that mitigation could be implemented during this seasonal activity period in order to reduce amphibian road-mortality rates; however, a study conducted on Fowler’s toad (Anaxyrus fowleri) cautioned that road mortality may not always be limited to seasonal migrations [15].

Yet, even within a taxon many of the studies conducted over multiple species found that road mortality was species-specific [16, 17]. For example, a study that explored variations in road mortality rates of 11 species of mammalian carnivore found that stone martens (Martes foina), European otters (Lutra lutra), and red foxes (Vulpes vulpes) were most frequently documented [18]. It was suggested that the higher number of roadkills among these species most likely reflected their life-history phenologies. Thus, understanding the life history traits and associated factors that make some species more vulnerable to wildlife-vehicle collisions than others would certainly allow us to devise more targeted mitigation along certain roads. In fact, many of the research studies undertaken on road mortality tended to explore the differences between and within species with the intention of informing more targeted mitigation. For instance, studies considered in this review found that a variety of species-associated factors influenced the rate of wildlife-vehicle collisions, such as age (particularly dispersing juveniles; [19, 20, 21, 22, 23]), activity patterns (such as nocturnal and migratory activities; [6, 7, 24, 25]), season (primarily breeding season; [12, 26, 27, 28, 29]), gender (such as males ranging further in the breeding season in search a mate; [23, 26, 30, 31, 32]), diet preferences (e.g., one study found that omnivorous mammals and herbivorous birds were most vulnerable; [33]), mobility (including low-flying species; [15, 21, 34, 35]), behavioral responses (e.g., certain species do not respond to oncoming traffic; [15, 36]), and home range size (i.e., the larger the home range the higher the probability of crossing a road; [37]). Another study found that species that were more inconspicuous on the roads were more vulnerable to wildlife-vehicle collisions [38].

A number of studies also demonstrated the importance of habitat variables, such as distance of suitable habitat from the road and local topography. For example, one study that used radio-telemetry to track two species of snake in the Grasslands National Park in Canada found that road mortality rates were positively correlated with distance from their hibernacula [39]. Such studies can certainly be used to inform future road development. Furthermore, a total of six studies related increased road mortality rates of wildlife, not including amphibians, with distances from wetlands or water sources (e.g., [17]). Research included in this review also highlighted two concerns, 1) that maintained road-side habitat provided quality forage, nesting resources or breeding habitat for many species, thus attracting wildlife, which in turn increased their risk of wildlife-vehicle collisions [21, 31, 32, 40], and 2) that road mortality increased with proximity to a protected area [9, 10]. The latter indicates that any existing road or future road development in proximity to a protected area has the potential to impact the species that area may be attempting to protect.

In addition, many road mortality studies have tried to identify road characteristics that may be more readily associated with wildlife-vehicle-collisions. The most predominate characteristic among the studies reviewed was traffic volume. A total of 22 of the 64 studies conducted found that increasing traffic volume was positively correlated with road mortality rates of wildlife across all taxonomic groups. Furthermore, some national parks have already demonstrated that an increase in traffic volume as a result of a rise in tourism has led to an equivalent increase in wildlife-vehicle-collisions [10, 19, 41]. With tourism numbers expected to continue to escalate, there are real concerns for how this trend will impact wildlife populations in protected areas.

Finally, other road characteristics that were studied included gap width [42, 43, 44] and traffic speed [27, 43, 44], both found to be positively correlation with road mortality, and road sinuosity with straighter roads leading to more wildlife-vehicle collisions [32, 43, 45]. Transportation authorities and conservation practitioners can use these kinds of data to inform roadway design and develop targeted strategies that will reduce wildlife encounters.

Indirect Impacts of Roads on Wildlife

The presence of roads can have a wide variety of indirect impacts on wildlife ranging from changes in habitat quality to influencing behavior. Of the 82 studies that explored the indirect impacts of roads on wildlife, 30% explored whether the abundance and distribution of wildlife near roads varied due to behavioral avoidance (known as displacement), 29% investigated the fitness consequences associated with wildlife being near roads, 29% focused on the ability of wildlife to physically and behaviorally cross roads, and the remaining 12% looked into the habituation or attraction of wildlife to roads.

The ability or frequency at which individuals cross roads will likely remain a priority in the field of road ecology, as a road can reduce landscape permeability by acting as a barrier or filter to movement and, therefore, has the potential to influence population persistence. One study highlighted that the barrier effects of roads are a particular concern for species that migrate, such as the pronghorn (Antilocapra Americana) [46]. Of the studies included in this review, nine identified the characteristics of road that influenced permeability, such as traffic volume [46, 47, 48], road width or the number of lanes [47, 49], and road surface type. For the latter, five studies determined that paved roads impeded the movement of wildlife more than unpaved roads (including a number of species of reptiles and chimpanzees, Pan troglodytes) [47, 50, 51, 52, 53]. In contrast, another study found that gopher tortoises (Gopherus Polyphemus) could not physically cross a sand road with deep vehicle ruts [54].

Such research studies further demonstrated that not only can the barrier-effects of roads be species-specific across multiple taxa [47, 49, 55], they can influence some individuals within a species more than others [48, 54]. Such differences were related to gender [48, 50], age and/or body size (with smaller individuals being more restricted; [54]), and life history stage (such as the breeding season; [48, 50]). For example, in Kibale National Park in Uganda female chimpanzees with dependent young were less likely to cross a high-traffic asphalt road than other troop members [50].

But roads do not just influence landscape permeability for wildlife. A road can also affect the abundance and distribution of individuals within habitats adjacent to it (known as the road-effect zone; [4]). A total of 19 studies confirmed that a wide variety of birds, mammals, reptiles, and invertebrates could be displaced from habitats in proximity to roads. Nevertheless, only three of these studies determined the extent of the road-effect zone [56, 57, 58]. This low number was surprising considering the increasing popularity of tools, such a simulation models (see Consequences of Road Networks on Wildlife Populations section below) that are used to explore the impact of road networks on wildlife populations. The accuracy of such models depends on the inclusion of the road-effect zone as it is a more realistic representation of the potential habitat loss associated with roads. For example, a study on Tawny owls (Strix aluco) in rural Portugal found that owl density could be impacted up to 2 km from a major road [56]. Even for a common and widespread species such an impact has the potential to influence population numbers and persistence in the area.

Instead, more studies focused on determining whether certain characteristics of a road influenced the extent to which wildlife avoided roadside habitats. Nine studies confirmed that species diversity and abundance near roads decreased with increasing traffic volume and three of these studies further attempted to quantify traffic volume thresholds at which species density began to decrease [57, 59, 60]. For example, a study in Canada revealed that grizzly bears (Ursus arctos) were more likely to use areas near roads with fewer than 20 vehicles per day [59]. It is studies like these that are essential for informing effective mitigation, such as traffic calming strategies (see Survey Design and Mitigation section).

The road effect zone can also be influenced by the degradation of air, land and water due to pollution from salt, sediment, chemical run-off, dust, noise, and light. Such road-related pollution can cause loss of habitat by making the area within the road effect zone unsuitable for wildlife. In this review, 23 studies explored the impact of road-related pollution on birds, mammals, amphibians, and invertebrates. Six of these studies determined that pollution caused the displacement of wildlife from roadside habitat, while the remaining 17 studies investigated the fitness consequences of polluted roadside habitats to wildlife. Among these 23 studies, 70% considered the impacts of noise pollution. For species that use vocalizations to undertaken crucial life history stages (such as song birds and amphibians; [61, 62]) or reply on sound to avoid predators, navigate and forage (such as bats and amphibians; [63, 64]), any noise that can mask sound can have a detrimental impact. For example, three studies demonstrated that bird diversity decreased as a result of road-related noise [65, 66, 67]. Another study showed that the foraging efficiency of Daubenton’s bats (Myotis daubentonii) decreased when vehicle noise masked their echolocation calls, which in turn lead to the avoidance of roadside habitat [64]. A further three studies confirmed that road-related noise influenced the survival and breeding success of wildlife. Among birds, such noise led to smaller clutch sizes [68] and reduced longevity [69], and among amphibians, it induced a stress response and impaired the wood frog’s (Lithobates sylvaticus) ability to attract mates [62]. Similarly, a study on Stephen’s kangeroo rat (Dipodomys stephensi) found that the sound of passing vehicles induced foot drumming and thus raised concerns that engaging in such false responses could potentially be energetically and biologically costly [70]. It is studies like these that emphasize the importance of the soundscape and mitigation should take noise pollution from roads into account, although it should be noted that certain species are more sensitive to noise pollution than other. For example, six studies found that road-related noise did not appear influence their study species, including arboreal rainforest mammals [71], a large ungulate [72], a bird of prey [73], certain song birds [74, 75], and some amphibian species [76].

Of the remaining studies to explore the impact of road pollution on wildlife, one explored light pollution [77], one considered heavy metals and five investigated the fitness consequences of deicing salt. The latter studies all demonstrated that amphibian larval survival was reduced by increases in salinity [78, 79, 80, 81]. In fact, one study showed that for Rana temporaria, even the smallest increase in the salt concentration (500 mg/L) could cause tadpole mortality [81].

Consequences of Road Networks on Wildlife Populations

Only 10% of the studies included in this review considered the implications of road networks on wildlife. As road networks have the ability to isolate populations, disconnect resource networks, and cause the irreversible degradation of habitat at a landscape scale, it is essential that we understand how existing road networks impact wildlife and how increasing road density may influence local and regional population persistence. The lack of current research is likely due to the logistical and economic limitations associated with such landscape-scale field studies. Yet, there is an increasing trend toward the use of predictive models to estimate and explore the impacts of road networks. For example, six studies presented models that were developed specifically to provide insights into the implications of road networks. Four of these studies focused on landscape connectivity, including a model that explored how road networks could influence pronghorn migration [82] and one that was developed to predict where black bears (Ursus americanus) were likely to encounter roads [83]. Another study created a predictive model that estimated the persistence of populations in a road-fragmented landscape [84], and finally a study simulated the genetic consequences created by the barrier-effects of roads across the road network [85]. For those species that have distinct movement corridors to access resources, disperse to maintain their social structure, or seasonally migrate, the presence of a road network within the landscape may have two, not necessarily exclusive, consequences. The first being, if roads act as barriers to movement, individuals may not be able to access critical food resources, breeding grounds, hibernacula, or avoid inbreeding. Predictive models may, therefore, be essential in identifying and providing insights into such issues. These insights can then be used to inform road planning and potentially identify alternative routes, incorporate targeted mitigation measures into the road construction stage (such as crossing structures and balancing ponds), and identify critical locations for the restoration and creation of new habitats.

Of the remaining studies relating to road networks, five studies were able to explore how road density impacted large mammals, primarily by using GPS collars that enabled them to map movement patterns across landscapes (e.g., caribou, moose, and lynx were among the species studied). These research studies demonstrated that for species that are well-adapted to moving through a heterogeneous landscape, road networks can still impede their movement [86], alter their behavior and activity patterns [87, 88], and influence their habitat use [89]. But even without any changes in habitat use and/or behavior, the more roads an individual has to cross within its home range, the greater the probability of a wildlife-vehicle collision [90, 91].

A number of studies also highlighted that road networks can augment the negative implications of roads. For example, one study on caribou showed that calf survival in proximity to roads decreased because predators habitually used roads as movement corridors [92]. Another emerging issue is that roads bring humans more readily into contact with wildlife. Three studies demonstrated that as road networks develop and increase accessibility of the landscape, the opportunities for hunting also increased [93, 94, 95]. Essentially, road networks increased the proportion of the landscape that could be accessed by humans and in so doing decreased the amount of available wildlife refuge [93]. For example, a notable increase in the bushmeat trade is threatening the population persistence of game species [95]. Studies included in this review showed that this increase was found to be associated with the presence of roads and not population growth in the local communities [94, 95]. Thus, as road development continues to fragment the remaining tracts of natural habitat, there is likely to be an initial and evident loss of species. One study also raised concerns that the development of road networks will impact protected areas [94]. Despite being afforded protection, the presence of roads can give humans better access to such areas and surrounding habitats and, therefore, access to species of conservation concern. Thus, poaching and the illegal pet trade are effectively made easier where roads are present and can potentially impede and jeopardize conservation efforts for many threatened and endangered species.

Survey Design and Mitigation

Among the 215 studies reviewed, 27% were focused on survey design and mitigation, not including the six studies that presented innovative predictive models referred to in the section above. The majority of these studies (38 of 58) involved the evaluation of proposed mitigation. One commonly used practice is the implementation of wildlife crossing structures, which are designed to allow focal species to move across roads safely. Nevertheless, despite many structures being constructed along roads all over the world, up until recently there has been a lot of uncertainty surrounding their effectiveness. Thus, it is not surprising that there has been a sudden influx of research studies that specifically evaluate such crossing structures (i.e., 26 studies included in this review). Nineteen studies evaluated the effectiveness of underpasses (culverts and tunnels) and seven evaluated overpasses (“green” bridges and tree canopy linkages or gantries). These studies demonstrated that there was a considerable amount of variation in the effectiveness of crossing structures. For example, while one study reported a 79% decline in road-related mortality of amphibians with crossing structures present [96], another study found that amphibian mortality remained the same with or without structures [97].

Overall, many studies agreed that crossing structures alone where not as effective as expected [98, 99, 100, 101, 102], and those studies that reported road mortality rates more often saw reductions ranging from 10 to 20% [103, 104]. Nevertheless, two studies speculated that crossing structures were still biologically effective as low crossing frequencies may be enough to maintain functional connectivity [105, 106]. A study on bear species in Canada supported this theory by revealing that while structures were not frequently used, overall use was sufficient to ensure gene flow between populations [107]. It is this level of connectivity that potentially justifies the continued implementation of crossing structures as mitigation, particularly where rare and endangered species are concerned [108].

Another commonly used mitigation strategy is to put up fencing along roadsides, which can either prevent wildlife from crossing a road or funnel them towards crossing structures. A total of five studies explored how effective fencing was, and among these studies, reductions in road-related mortality still varied considerably, ranging from 50 to 98% [109, 110]. They identified factors that may have influenced the effectiveness of the fencing to be species-specific use, length of fencing, and presence of intersections [109, 111, 112]. Two studies even cautioned that fencing was ineffective unless it was continually maintained and breaches in the fencing were repaired in a timely manner [111, 113]. Furthermore, many studies suggested that road-related mortality could be further reduced when crossing structures were combined with fencing [97, 114, 115]. Moreover, one study on koalas (Phascolarctos cinereus) in Australia emphasized that selecting a single mitigation option was not economically viable [114].

A third mitigation strategy is to implement traffic calming measures, such as speed bumps, lowing speed limits or using warning signs. Three studies included in this review reported that reducing speed limits effectively decreased wildlife-vehicle collisions [116], but two further studies cautioned that traffic calming devices, such a roundabouts, chicanes and speed bumps, were more effective than signage alone [117, 118]. Another study suggested that warning signs were more effective when used sparingly, in other words, only when the risk of wildlife-vehicle collisions was high (i.e., during migration or the breeding season, or when visibility was poor) [119].

Finally, three studies evaluated alternative or innovative mitigation strategies. One study explored whether having a vegetative medium facilitated the movement of small mammals, reptiles, and amphibians across roads; however, the results suggested this strategy was ineffective [120]. Another study tested an odor repellent intended to deter wildlife from crossing roads. They found that the repellent successfully reduced the crossing activity of larger mammals commonly reported in wildlife-vehicle collisions [121]. The third study evaluated the effectiveness of pole barriers at preventing birds from crossing roads at vehicle height. This study reported that 94% of birds, including a wide variety of species, shifted their flight paths as a result of the pole barrier being present [122]. The development and evaluation of mitigation measures such as these remains one of the most crucial components of road ecology, as there is still a lot of scope to devise and modify more effective strategies [123, 124]. It is, therefore, important that such research and development continues and is encouraged.

Overall, many studies were in agreement that the effectiveness of crossing structures, fencing, deterrents, and traffic calming measures, was species-specific [96, 98, 105, 121]. A generic “one-size fits all” crossing structure or deterrent does not exist. These findings reiterate that transportation authorities and conservation practitioners will have to implement multiple forms of mitigation if they are to reduce wildlife-vehicle collisions and maintain landscape connectivity for a range of species.

Of the remaining studies included in this review and category, 20 focused on evaluating, modifying, and developing survey techniques to better estimate road-related mortality, displacement, and road crossing activity. For example, four studies postulated that the way crossing structures are currently selected and even evaluated is biased and called for more effective mitigation selection and surveillance protocols [104, 125, 126, 127]. Similarly, a study conducted in a conservation area in South Africa revealed that the way roadkill surveys are undertaken can strongly influence road mortality estimates [128]. They identified that survey speed, time of day, and number of observers were all important factors to consider and highly recommended the use of standardized protocols. Another study determined that variations in carcass persistence could also bias survey results, as persistence was much lower in small animals and easily influenced weather conditions [129]. Other studies flagged up survey interval [130], scavenger removal [131] and species detectability [132, 133] as factors that would influence survey results. Such biased surveys are particularly concerning if mortality estimates are used to determine the impact of roads and road networks on a species, and particularly when species of concern are involved. Furthermore, the implementation of mitigation measures, and certainly the placement of crossing structures, is often based on the identification of mortality hotspots or distinct patterns of mortality along a road. Thus, if the surveys used to inform mitigation are inaccurate, then it is likely that any mitigation implemented will be ineffective.

Finally, the development and use of models continues to gain popularity with a further nine studies presenting models that could be used to estimate road-related mortality [134, 135] and identify potential mortality hotspots based on surrounding habitat and landscape features [40, 136, 137, 138, 139]. For example, one model was constructed specifically to identify ideal locations for crossing structures for the endangered Florida panther (Puma concolor coryi) using GPS collar data and wildlife-vehicle collision reports [140]. Another model was developed to identify habitat patches that could be restored and define wildlife corridor locations that would have the best chance of increasing landscape connectivity [141]. Again predictive models may prove to be an essential tool in the road planning and mitigation stages of future road development.

Conclusions

One very clear trend that was evident from this review was that while many studies speculate that the negative impacts of roads can have consequences for wildlife populations due to increased mortality rates, displacement, habitat degradation, and loss of landscape connectivity, very few studies explored the population-level consequences. Moreover, the majority of studies investigated the impact of individual road-associated factors, such as road mortality or road avoidance, but only a handful of studies considered the impact of more than one factor combined. Based on the research conducted, it is likely that most species will not be impacted by a single factor. For example, among the studies to research freshwater turtles, two studies found that roads were a source of mortality due to wildlife-vehicle collisions [120, 142] and two studies determined that the movement of turtles was physically hindered by the roads themselves [52, 143]. Yet, it is the combination of both direct and indirect factors that will provide us with a better understanding of the barrier effects of roads on freshwater turtles. Furthermore, if we are to effectively mitigate the impacts of roads, we need to understand the full extent of the impact. For example, using fencing may effectively reduce the number of pronghorns killed by vehicles on roads, but it may also reduce landscape permeability by hindering their ability to migrate across the landscape [46, 82]. Considering the large amount of research studies that has been conducted in the last three decades on a wide range of road-related factors and across a diverse array of species, there is certainly an opportunity to use the data to explore the cumulative impacts of roads on wildlife.

One emerging aspect of road ecology that warrants encouragement is the continued development of predictive landscape-scale models. Such models have great potential to be used as tools for the assessment, prevention, and mitigation of road networks. By having such models in their toolbox, transportation authorities and conservation practitioners may be better equipped to address the ecological implications of roads and proposed road development.

Notes

Acknowledgements

Thank you to Aaron McAlexander and Matthew Hale for reading drafts of this manuscript.

Compliance with Ethical Standards

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Supplementary material

40823_2017_20_MOESM1_ESM.docx (46 kb)
Appendix A(DOCX 45 kb)

References

  1. 1.
    Bennett VJ, Smith WP, Betts MG. Toward understanding the ecological impact of transportation corridors. Report No.: Gen. Tech. Rep. PNW-GTR-846. Portland: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 2011. 40 p.Google Scholar
  2. 2.
    Ward AI, Dendy J, Cowan DP. Mitigating impacts of roads on wildlife: an agenda for the conservation of priority European protected species in Great Britain. Eur J Wildl Res. 2015;61(2):199–211. doi:10.1007/s10344-015-0901-0.CrossRefGoogle Scholar
  3. 3.
    Bennett AF. Roads, roadsides and wildlife conservation: a review. In: Saunders DA, Hobbs RJ, editors. Nature conservation 2: the role of corridors. Chipping Norton: Surrey Beatty & Sons Pty Limited; 1991. p. 99–117.Google Scholar
  4. 4.
    Forman RTT, Sperling D, Bissonette JA, Clevenger AP, Cutshall CD, Dale VH, et al. Road ecology: science and solutions. Washington, DC: Island Press; 2003.Google Scholar
  5. 5.
    Fahrig L, Rytwinski T. Effects of roads on animal abundance: an empirical review and synthesis. Ecol Soc. 2009;14(1):20pp-pp.Google Scholar
  6. 6.
    Lagos L, Picos J, Valero E. Temporal pattern of wild ungulate-related traffic accidents in northwest Spain. Eur J Wildl Res. 2012;58(4):661–8. doi:10.1007/s10344-012-0614-6.CrossRefGoogle Scholar
  7. 7.
    Niemi M, Tiilikainen R, Nummi P. Moose-vehicle collisions occur earlier in warm springs. Acta Theriol. 2013;58(4):341–7. doi:10.1007/s13364-013-0151-z.CrossRefGoogle Scholar
  8. 8.
    Zuberogoitia I, del Real J, Torres JJ, Rodriguez L, Alonso M, Zabala J. Ungulate vehicle collisions in a peri-urban environment: consequences of transportation infrastructures planned assuming the absence of ungulates. Plos One. 2014;9(9). doi:10.1371/journal.pone.0107713.
  9. 9.
    Carvalho F, Mira A. Comparing annual vertebrate road kills over two time periods, 9 years apart: a case study in Mediterranean farmland. Eur J Wildl Res. 2011;57(1):157–74. doi:10.1007/s10344-010-0410-0.CrossRefGoogle Scholar
  10. 10.
    Garriga N, Santos X, Montori A, Richter-Boix A, Franch M, Llorente GA. Are protected areas truly protected? The impact of road traffic on vertebrate fauna. Biodivers Conserv. 2012;21(11):2761–74. doi:10.1007/s10531-012-0332-0.CrossRefGoogle Scholar
  11. 11.
    Wang Y, Piao ZJ, Guan L, Wang XY, Kong YP, Chen J. Road mortalities of vertebrate species on Ring Changbai Mountain Scenic Highway, Jilin Province, China. Northwest J Zool. 2013;9(2):399–409.Google Scholar
  12. 12.
    Garrah E, Danby RK, Eberhardt E, Cunnington GM, Mitchell S. Hot spots and hot times: wildlife road mortality in a regional conservation corridor. Environ Manag. 2015;56(4):874–89.CrossRefGoogle Scholar
  13. 13.
    Tok CV, Ayaz D, Cicek K. Road mortality of amphibians and reptiles in the Anatolian part of Turkey. Turk J Zool. 2011;35(6):851–7. doi:10.3906/zoo-0911-97.Google Scholar
  14. 14.
    D’Amico M, Roman J, de los Reyes L, Revilla E. Vertebrate road-kill patterns in Mediterranean habitats: who, when and where. Biol Conserv. 2015;191:234–42. doi:10.1016/j.biocon.2015.06.010.CrossRefGoogle Scholar
  15. 15.
    Timm BC, McGarigal K. Fowler’s toad (Anaxyrus fowleri) activity patterns on a roadway at Cape Cod National Seashore. J Herpetol. 2014;48(1):111–6. doi:10.1670/12-202.CrossRefGoogle Scholar
  16. 16.
    Matos C, Sillero N, Argana E. Spatial analysis of amphibian road mortality levels in northern Portugal country roads. Amphibia-Reptilia. 2012;33(3–4):469–83. doi:10.1163/15685381-00002850.CrossRefGoogle Scholar
  17. 17.
    Medinas D, Tiago Marques J, Mira A. Assessing road effects on bats: the role of landscape, road features, and bat activity on road-kills. Ecol Res. 2013;28(2):227–37. doi:10.1007/s11284-012-1009-6.CrossRefGoogle Scholar
  18. 18.
    Cervinka J, Riegert J, Grill S, Salek M. Large-scale evaluation of carnivore road mortality: the effect of landscape and local scale characteristics. Mammal Res. 2015;60(3):233–43. doi:10.1007/s13364-015-0226-0.CrossRefGoogle Scholar
  19. 19.
    Mkande FX, Chansa W. Changes in temporal and spatial pattern of road kills along the Lusaka-Mongu (M9) highway, Kafue National Park, Zambia. S Afr J Wildl Res. 2011;41(1):68–78.CrossRefGoogle Scholar
  20. 20.
    da Rosa CA, Bager A. Seasonality and habitat types affect roadkill of neotropical birds. J Environ Manag. 2012;97:1–5. doi:10.1016/j.jenvman.2011.11.004.CrossRefGoogle Scholar
  21. 21.
    Jochimsen DM, Peterson CR, Harmon LJ. Influence of ecology and landscape on snake road mortality in a sagebrush-steppe ecosystem. Anim Conserv. 2014;17(6):583–92. doi:10.1111/acv.12125.CrossRefGoogle Scholar
  22. 22.
    Kovar R, Brabec M, Vita R, Bocek R. Mortality rate and activity patterns of an Aesculapian snake (Zamenis longissimus) population divided by a busy road. J Herpetol. 2014;48(1):24–33. doi:10.1670/12-090.CrossRefGoogle Scholar
  23. 23.
    Vujovic A, Ikovic V, Golubovic A, Dordevic S, Pesic V, Tomovic L. Effects of fires and roadkills on the isolated population of Testudo hermanni Gmelin, 1789 (Reptilia: Testudinidae) in Central Montenegro. Acta Zool Bulg. 2015;67(1):75–84.Google Scholar
  24. 24.
    Olson DD, Bissonette JA, Cramer PC, Bunnell KD, Coster DC, Jackson PJ. How does variation in winter weather affect deer-vehicle collision rates? Wildl Biol. 2015;21(2):80–7. doi:10.2981/wlb.00043.CrossRefGoogle Scholar
  25. 25.
    Bullock KL, Malan G, Pretorius MD. Mammal and bird road mortalities on the Upington to Twee Rivieren main road in the southern Kalahari, South Africa. Afr Zool. 2011;46(1):60–71.CrossRefGoogle Scholar
  26. 26.
    Palazon S, Melero Y, Gomez A. Lopez de Luzuriaga J, Podra M, Gosalbez J. Causes and patterns of human-induced mortality in the critically endangered European mink Mustela lutreola in Spain. Oryx. 2012;46(4):614–6. doi:10.1017/s0030605312000920.CrossRefGoogle Scholar
  27. 27.
    Farmer RG, Brooks RJ. Integrated risk factors for vertebrate roadkill in southern Ontario. J Wildl Manag. 2012;76(6):1215–24. doi:10.1002/jwmg.358.CrossRefGoogle Scholar
  28. 28.
    Mizuta T. Moonlight-related mortality: lunar conditions and roadkill occurrence in the Amami Woodcock Scolopax mira. Wilson J Ornithol. 2014;126(3):544–52.CrossRefGoogle Scholar
  29. 29.
    Crawford BA, Maerz JC, Nibbelink NP, Buhlmann KA, Norton TM, Albeke SE. Hot spots and hot moments of diamondback terrapin road-crossing activity. J Appl Ecol. 2014;51(2):367–75. doi:10.1111/1365-2664.12195.CrossRefGoogle Scholar
  30. 30.
    Schwab AC, Zandbergen PA. Vehicle-related mortality and road crossing behavior of the Florida panther. Appl Geogr. 2011;31(2):859–70. doi:10.1016/j.apgeog.2010.10.015.CrossRefGoogle Scholar
  31. 31.
    Boves TJ, Belthoff JR. Roadway mortality of barn owls in Idaho, USA. J Wildl Manag. 2012;76(7):1381–92. doi:10.1002/jwmg.378.CrossRefGoogle Scholar
  32. 32.
    de Freitas CH, Justino CS, Setz EZF. Road-kills of the giant anteater in south-eastern Brazil: 10 years monitoring spatial and temporal determinants. Wildl Res. 2014;41(8):673–80. doi:10.1071/wr14220.CrossRefGoogle Scholar
  33. 33.
    Cook TC, Blumstein DT. The omnivore’s dilemma: diet explains variation in vulnerability to vehicle collision mortality. Biol Conserv. 2013;167:310–5. doi:10.1016/j.biocon.2013.08.016.CrossRefGoogle Scholar
  34. 34.
    Soluk DA, Zercher DS, Worthington AM. Influence of roadways on patterns of mortality and flight behavior of adult dragonflies near wetland areas. Biol Conserv. 2011;144(5):1638–43. doi:10.1016/j.biocon.2011.02.015.CrossRefGoogle Scholar
  35. 35.
    Hartmann PA, Hartmann MT, Martins M. Snake road mortality in a protected area in the Atlantic forest of southeastern Brazil. S Am J Herpetol. 2011;6(1):35–42.CrossRefGoogle Scholar
  36. 36.
    DeVault TL, Blackwell BF, Seamans TW, Lima SL, Fernandez-Juricic E. Speed kills: ineffective avian escape responses to oncoming vehicles. Proc R Soc B Biol Sci. 2015;282(1801). doi:10.1098/rspb.2014.2188.
  37. 37.
    Grilo C, Sousa J, Ascensao F, Matos H, Leitao I, Pinheiro P et al. Individual spatial responses towards roads: implications for mortality risk. Plos One. 2012;7(9). doi:10.1371/journal.pone.0043811.
  38. 38.
    Loehr VJT. Road mortality in the greater padloper, Homopus femoralis (Testudinidae). Chelonian Conserv Biol. 2012;11(2):226–9.CrossRefGoogle Scholar
  39. 39.
    Fortney AN, Poulin RG, Martino JA, Parker DL, Somers CM. Proximity to hibernacula and road type influence potential road mortality of snakes in southwestern Saskatchewan. Can Field Nat. 2012;126(3):194–203.CrossRefGoogle Scholar
  40. 40.
    Santos SM, Lourenco R, Mira A, Beja P. Relative effects of road risk, habitat suitability, and connectivity on wildlife roadkills: the case of Tawny Owls (Strix aluco). Plos One. 2013;8(11). doi:10.1371/journal.pone.0079967.
  41. 41.
    Skorka P, Lenda M, Moron D, Martyka R, Tryjanowski P, Sutherland WJ. Biodiversity collision blackspots in Poland: separation causality from stochasticity in roadkills of butterflies. Biol Conserv. 2015;187:154–63. doi:10.1016/j.biocon.2015.04.017.CrossRefGoogle Scholar
  42. 42.
    Gu H, Dai Q, Wang Q, Wang Y. Factors contributing to amphibian road mortality in a wetland. Curr Zool. 2011;57(6):768–74.CrossRefGoogle Scholar
  43. 43.
    Barthelmess EL. Spatial distribution of road-kills and factors influencing road mortality for mammals in Northern New York State. Biodivers Conserv. 2014;23(10):2491–514. doi:10.1007/s10531-014-0734-2.CrossRefGoogle Scholar
  44. 44.
    Gagne SA, Bates JL, Bierregaard RO. The effects of road and landscape characteristics on the likelihood of a Barred Owl (Strix varia)-vehicle collision. Urban Ecosyst. 2015;18(3):1007–20. doi:10.1007/s11252-015-0465-5.CrossRefGoogle Scholar
  45. 45.
    Snow NP, Andelt WF, Stanley TR, Resnik JR, Munson L. Effects of roads on survival of San Clemente Island foxes. J Wildl Manag. 2012;76(2):243–52. doi:10.1002/jwmg.247.CrossRefGoogle Scholar
  46. 46.
    Seidler RG, Long RA, Berger J, Bergen S, Beckmann JP. Identifying impediments to long-distance mammal migrations. Conserv Biol. 2015;29(1):99–109. doi:10.1111/cobi.12376.PubMedCrossRefGoogle Scholar
  47. 47.
    Brehme CS, Tracey JA, McClenaghan LR, Fisher RN. Permeability of roads to movement of scrubland lizards and small mammals. Conserv Biol. 2013;27(4):710–20. doi:10.1111/cobi.12081.PubMedCrossRefGoogle Scholar
  48. 48.
    Beauchesne D, Jaeger JAG, St-Laurent M-H. Disentangling woodland caribou movements in response to clearcuts and roads across temporal scales. Plos One. 2013;8(11). doi:10.1371/journal.pone.0077514.
  49. 49.
    Ramos de Oliveira Jr PR, Alberts CC, Francisco MR. Impact of road clearings on the movements of three understory insectivorous bird species in the Brazilian Atlantic Forest. Biotropica. 2011;43(5):628–32. doi:10.1111/j.1744-7429.2010.00744.x.CrossRefGoogle Scholar
  50. 50.
    Cibot M, Bortolamiol S, Seguya A, Krief S. Chimpanzees facing a dangerous situation: a high-traffic asphalted road in the Sebitoli area of Kibale National Park, Uganda. Am J Primatol. 2015;77(8):890–900. doi:10.1002/ajp.22417.PubMedCrossRefGoogle Scholar
  51. 51.
    Robson LE, Blouin-Demers G. Eastern hognose snakes (Heterodon platirhinos) avoid crossing paved roads, but not unpaved roads. Copeia. 2013;3:507–11. doi:10.1643/ce-12-033.CrossRefGoogle Scholar
  52. 52.
    Proulx CL, Fortin G, Blouin-Demers G. Blanding’s turtles (Emydoidea blandingii) avoid crossing unpaved and paved roads. J Herpetol. 2014;48(2):267–71. doi:10.1670/12-176.CrossRefGoogle Scholar
  53. 53.
    Mulero-Pazmany M, D’Amico M, Gonzalez-Suarez M. Ungulate behavioral responses to the heterogeneous road-network of a touristic protected area in Africa. J Zool. 2016;298(4):233–40. doi:10.1111/jzo.12310.CrossRefGoogle Scholar
  54. 54.
    Gilson LN, Bateman PW. Stuck in a rut: potential costs of sand roads to gopher tortoises Gopherus polyphemus. Curr Zool. 2015;61(4):578–85.CrossRefGoogle Scholar
  55. 55.
    Thinh VT, Doherty Jr PF, Bui TH, Huyvaert KP. Road crossing by birds in a tropical forest in northern Vietnam. Condor. 2012;114(3):639–44. doi:10.1525/cond.2012.100199.CrossRefGoogle Scholar
  56. 56.
    Silva CC, Lourenco R, Godinho S, Gomes E, Sabino-Marques H, Medinas D, et al. Major roads have a negative impact on the Tawny Owl Strix aluco and the Little Owl Athene noctua populations. Acta Ornithol. 2012;47(1):47–54. doi:10.3161/000164512x653917.CrossRefGoogle Scholar
  57. 57.
    Nafus MG, Tuberville TD, Buhlmann KA, Todd BD. Relative abundance and demographic structure of Agassiz’s desert tortoise (Gopherus agassizii) along roads of varying size and traffic volume. Biol Conserv. 2013;162:100–6. doi:10.1016/j.biocon.2013.04.009.CrossRefGoogle Scholar
  58. 58.
    Clarke RT, Liley D, Sharp JM, Green RE. Building development and roads: implications for the distribution of Stone Curlews across the Brecks. Plos One. 2013;8(8). doi:10.1371/journal.pone.0072984.
  59. 59.
    Northrup JM, Pitt J, Muhly TB, Stenhouse GB, Musiani M, Boyce MS. Vehicle traffic shapes grizzly bear behaviour on a multiple-use landscape. J Appl Ecol. 2012;49(5):1159–67. doi:10.1111/j.1365-2664.2012.02180.x.CrossRefGoogle Scholar
  60. 60.
    Lian X, Zhang T, Cao Y, Su J, Thirgood S. Road proximity and traffic flow perceived as potential predation risks: evidence from the Tibetan antelope in the Kekexili National Nature Reserve, China. Wildl Res. 2011;38(2):141–6. doi:10.1071/wr10158.CrossRefGoogle Scholar
  61. 61.
    Hanna D, Blouin-Demers G, Wilson DR, Mennill DJ. Anthropogenic noise affects song structure in Red-winged Blackbirds (Agelaius phoeniceus). J Exp Biol. 2011;214(21):3549–56. doi:10.1242/jeb.060194.PubMedCrossRefGoogle Scholar
  62. 62.
    Tennessen JB, Parks SE, Langkilde T. Traffic noise causes physiological stress and impairs breeding migration behaviour in frogs. Conservation Physiology. 2014;2(1). doi:10.1093/conphys/cou032.
  63. 63.
    Lukanov S, Simeonovska-Nikolova D, Tzankov N. Effects of traffic noise on the locomotion activity and vocalization of the marsh frog, Pelophylax ridibundus. Northwest J Zool. 2014;10(2):359–64.Google Scholar
  64. 64.
    Luo JH, Siemers BM, Koselj K. How anthropogenic noise affects foraging. Glob Chang Biol. 2015;21(9):3278–89. doi:10.1111/gcb.12997.PubMedCrossRefGoogle Scholar
  65. 65.
    Summers PD, Cunnington GM, Fahrig L. Are the negative effects of roads on breeding birds caused by traffic noise? J Appl Ecol. 2011;48(6):1527–34. doi:10.1111/j.1365-2664.2011.02041.x.CrossRefGoogle Scholar
  66. 66.
    McClure CJW, Ware HE, Carlisle J, Kaltenecker G, Barber JR. An experimental investigation into the effects of traffic noise on distributions of birds: avoiding the phantom road. Proc R Soc B Biol Sci. 2013;280(1773). doi:10.1098/rspb.2013.2290.
  67. 67.
    Wiacek J, Polak M, Kucharczyk M, Bohatkiewicz J. The influence of road traffic on birds during autumn period: implications for planning and management of road network. Landsc Urban Plan. 2015;134:76–82. doi:10.1016/j.landurbplan.2014.10.016.CrossRefGoogle Scholar
  68. 68.
    Halfwerk W, Holleman LJM, Lessells CM, Slabbekoorn H. Negative impact of traffic noise on avian reproductive success. J Appl Ecol. 2011;48(1):210–9. doi:10.1111/j.1365-2664.2010.01914.x.CrossRefGoogle Scholar
  69. 69.
    Meillere A, Brischoux F, Ribout C, Angelier F. Traffic noise exposure affects telomere length in nestling house sparrows. Biol Lett. 2015;11(9). doi:10.1098/rsbl.2015.0559.
  70. 70.
    Shier DM, Lea AJ, Owen MA. Beyond masking: endangered Stephen’s kangaroo rats respond to traffic noise with footdrumming. Biol Conserv. 2012;150(1):53–8. doi:10.1016/j.biocon.2012.03.007.CrossRefGoogle Scholar
  71. 71.
    Byrnes P, Goosem M, Turton SM. Are less vocal rainforest mammals susceptible to impacts from traffic noise? Wildl Res. 2012;39(4):355–65. doi:10.1071/wr11010.Google Scholar
  72. 72.
    Brown CL, Hardy AR, Barber JR, Fristrup KM, Crooks KR, Angeloni LM. The effect of human activities and their associated noise on ungulate behavior. Plos One. 2012;7(7). doi:10.1371/journal.pone.0040505.
  73. 73.
    Grubb TG, Pater LL, Gatto AE, Delaney DK. Response of nesting Northern Goshawks to logging truck noise in northern Arizona. J Wildl Manag. 2013;77(8):1618–25. doi:10.1002/jwmg.607.CrossRefGoogle Scholar
  74. 74.
    Crino OL, Johnson EE, Blickley JL, Patricelli GL, Breuner CW. Effects of experimentally elevated traffic noise on nestling White-crowned Sparrow stress physiology, immune function and life history. J Exp Biol. 2013;216(11):2055–62. doi:10.1242/jeb.081109.PubMedCrossRefGoogle Scholar
  75. 75.
    Morgan GM, Wilcoxen TE, Rensel MA, Schoech SJ. Are roads and traffic sources of physiological stress for the Florida Scrub-jay? Wildl Res. 2012;39(4):301–10. doi:10.1071/wr11029.CrossRefGoogle Scholar
  76. 76.
    Herrera-Montes MI, Aide TM. Impacts of traffic noise on anuran and bird communities. Urban Ecosyst. 2011;14(3):415–27. doi:10.1007/s11252-011-0158-7.CrossRefGoogle Scholar
  77. 77.
    Szaz D, Horvath G, Barta A, Robertson BA, Farkas A, Egri A et al. Lamp-lit bridges as dual light-traps for the night-swarming mayfly, Ephoron virgo: interaction of polarized and unpolarized light pollution. Plos One. 2015;10(3). doi:10.1371/journal.pone.0121194.
  78. 78.
    Hopkins GR, French SS, Brodie Jr ED. Potential for local adaptation in response to an anthropogenic agent of selection: effects of road deicing salts on amphibian embryonic survival and development. Evol Appl. 2013;6(2):384–92. doi:10.1111/eva.12016.PubMedCrossRefGoogle Scholar
  79. 79.
    Petranka JW, Francis RA. Effects of road salts on seasonal wetlands: poor prey performance may compromise growth of predatory salamanders. Wetlands. 2013;33(4):707–15. doi:10.1007/s13157-013-0428-7.CrossRefGoogle Scholar
  80. 80.
    Zhang H, Wang Z, Zhang Y, Ding M, Li L. Identification of traffic-related metals and the effects of different environments on their enrichment in roadside soils along the Qinghai-Tibet highway. Sci Total Environ. 2015;521:160–72. doi:10.1016/j.scitotenv.2015.03.054.PubMedCrossRefGoogle Scholar
  81. 81.
    Dananay KL, Krynak KL, Krynak TJ, Benard MF. Legacy of road salt: apparent positive larval effects counteracted by negative postmetamorphic effects in wood frogs. Environ Toxicol Chem. 2015;34(10):2417–24. doi:10.1002/etc.3082.PubMedCrossRefGoogle Scholar
  82. 82.
    Poor EE, Loucks C, Jakes A, Urban DL. Comparing habitat suitability and connectivity modeling methods for conserving pronghorn migrations. Plos One. 2012;7(11). doi:10.1371/journal.pone.0049390.
  83. 83.
    Cushman SA, Lewis JS, Landguth EL. Evaluating the intersection of a regional wildlife connectivity network with highways. Mov Ecol. 2013;1(1):12. doi:10.1186/2051-3933-1-12.PubMedPubMedCentralCrossRefGoogle Scholar
  84. 84.
    Bojkovic N, Bozin V, Petrovic M, Anic I. Spatially continuous modeling approach for population persistence in road-fragmented landscapes. Appl Math Model. 2015;39(17):5174–85. doi:10.1016/j.apm.2015.03.039.CrossRefGoogle Scholar
  85. 85.
    Jackson ND, Fahrig L. Relative effects of road mortality and decreased connectivity on population genetic diversity. Biol Conserv. 2011;144(12):3143–8. doi:10.1016/j.biocon.2011.09.010.CrossRefGoogle Scholar
  86. 86.
    Beyer HL, Ung R, Murray DL, Fortin M-J. Functional responses, seasonal variation and thresholds in behavioural responses of moose to road density. J Appl Ecol. 2013;50(2):286–94. doi:10.1111/1365-2664.12042.CrossRefGoogle Scholar
  87. 87.
    Laurian C, Dussault C, Ouellet J-P, Courtois R, Poulin M. Interactions between a large herbivore and a road network. Ecoscience. 2012;19(1):69–79. doi:10.2980/19-1-3461.CrossRefGoogle Scholar
  88. 88.
    Leblond M, Dussault C, Ouellet J-P. Impacts of human disturbance on large prey species: do behavioral reactions translate to fitness consequences? Plos One. 2013;8(9). doi:10.1371/journal.pone.0073695.
  89. 89.
    Basille M, Van Moorter B, Herfindal I, Martin J, Linnell JDC, Odden J et al. Selecting habitat to survive: the impact of road density on survival in a large carnivore. Plos One. 2013;8(7). doi:10.1371/journal.pone.0065493.
  90. 90.
    Morelle K, Lehaire F, Lejeune P. Spatio-temporal patterns of wildlife-vehicle collisions in a region with a high-density road network. Nat Conserv Bulg. 2013;5:53–73. doi:10.3897/natureconservation.5.4634.CrossRefGoogle Scholar
  91. 91.
    Rhodes JR, Lunney D, Callaghan J, McAlpine CA. A few large roads or many small ones? How to accommodate growth in vehicle numbers to minimise impacts on wildlife. Plos One. 2014;9(3). doi:10.1371/journal.pone.0091093.
  92. 92.
    Dussault C, Pinard V, Ouellet J-P, Courtois R, Fortin D. Avoidance of roads and selection for recent cutovers by threatened caribou: fitness-rewarding or maladaptive behaviour? Proc R Soc B Biol Sci. 2012;279(1746):4481–8. doi:10.1098/rspb.2012.1700.CrossRefGoogle Scholar
  93. 93.
    Vanthomme H, Kolowski J, Korte L, Alonso A. Distribution of a community of mammals in relation to roads and other human disturbances in Gabon, Central Africa. Conserv Biol. 2013;27(2):281–91. doi:10.1111/cobi.12017.PubMedPubMedCentralCrossRefGoogle Scholar
  94. 94.
    Suarez E, Zapata-Rios G, Utreras V, Strindberg S, Vargas J. Controlling access to oil roads protects forest cover, but not wildlife communities: a case study from the rainforest of Yasuni Biosphere Reserve (Ecuador). Anim Conserv. 2013;16(3):265–74. doi:10.1111/j.1469-1795.2012.00592.x.CrossRefGoogle Scholar
  95. 95.
    Fa JE, Olivero J, Farfan MA, Marquez AL, Duarte J, Nackoney J, et al. Correlates of bushmeat in markets and depletion of wildlife. Conserv Biol. 2015;29(3):805–15. doi:10.1111/cobi.12441.PubMedCrossRefGoogle Scholar
  96. 96.
    Niemi M, Jaaskelainen NC, Nummi P, Makela T, Norrdahl K. Dry paths effectively reduce road mortality of small and medium-sized terrestrial vertebrates. J Environ Manag. 2014;144:51–7. doi:10.1016/j.jenvman.2014.05.012.CrossRefGoogle Scholar
  97. 97.
    Cunnington GM, Garrah E, Eberhardt E, Fahrig L. Culverts alone do not reduce road mortality in anurans. Ecoscience. 2014;21(1):69–78. doi:10.2980/21-1-3673.CrossRefGoogle Scholar
  98. 98.
    Gagnon JW, Dodd NL, Ogren KS, Schweinsburg RE. Factors associated with use of wildlife underpasses and importance of long-term monitoring. J Wildl Manag. 2011;75(6):1477–87. doi:10.1002/jwmg.160.CrossRefGoogle Scholar
  99. 99.
    Kaphegyi TAM, Dees M, Zlatanova D, Ueffing C, Dutsov A, Kaphegyi U. Rapid assessment of linear transport infrastructure in relation to the impact on landscape continuity for large ranging mammals. Biodivers Conserv. 2013;22(1):153–68. doi:10.1007/s10531-012-0409-9.CrossRefGoogle Scholar
  100. 100.
    Soanes K, Lobo MC, Vesk PA, McCarthy MA, Moore JL, van der Ree R. Movement re-established but not restored: inferring the effectiveness of road-crossing mitigation for a gliding mammal by monitoring use. Biol Conserv. 2013;159:434–41. doi:10.1016/j.biocon.2012.10.016.CrossRefGoogle Scholar
  101. 101.
    Hamer AJ, van der Ree R, Mahony MJ, Langton T. Usage rates of an under-road tunnel by three Australian frog species: implications for road mitigation. Anim Conserv. 2014;17(4):379–87. doi:10.1111/acv.12105.CrossRefGoogle Scholar
  102. 102.
    Pell S, Jones D. Are wildlife overpasses of conservation value for birds? A study in Australian sub-tropical forest, with wider implications. Biol Conserv. 2015;184:300–9. doi:10.1016/j.biocon.2015.02.005.CrossRefGoogle Scholar
  103. 103.
    Russell TC, Herbert CA, Kohen JL, Cooper D. The incidence of road-killed possums in the Ku-ring-gai area of Sydney. Aust J Zool. 2013;61(1):87–94. doi:10.1071/zo12118.CrossRefGoogle Scholar
  104. 104.
    Lesbarreres D, Fahrig L. Measures to reduce population fragmentation by roads: what has worked and how do we know? Trends Ecol Evol. 2012;27(7):374–80. doi:10.1016/j.tree.2012.01.015.PubMedCrossRefGoogle Scholar
  105. 105.
    Goldingay RL, Rohweder D, Taylor BD. Will arboreal mammals use rope-bridges across a highway in eastern Australia? Aust Mammal. 2013;35(1):30–8. doi:10.1071/am12006.CrossRefGoogle Scholar
  106. 106.
    Teixeira FZ, Printes RC, Godoy Fagundes JC, Alonso AC, Kindel A. Canopy bridges as road overpasses for wildlife in urban fragmented landscapes. Biota Neotrop. 2013;13(1):117–23.CrossRefGoogle Scholar
  107. 107.
    Sawaya MA, Kalinowski ST, Clevenger AP. Genetic connectivity for two bear species at wildlife crossing structures in Banff National Park. Proc R Soc B Biol Sci. 2014;281(1780). doi:10.1098/rspb.2013.1705.
  108. 108.
    Clevenger AP. Mitigating highways for a ghost: data collection challenges and implications for managing wolverines and transportation corridors. Northwest Sci. 2013;87(3):257–64. doi:10.3955/046.087.0307.CrossRefGoogle Scholar
  109. 109.
    Ford AT, Clevenger AP, Huijser MP, Dibb A. Planning and prioritization strategies for phased highway mitigation using wildlife-vehicle collision data. Wildl Biol. 2011;17(3):253–65. doi:10.2981/09-051.CrossRefGoogle Scholar
  110. 110.
    Bissonette JA, Rosa S. An evaluation of a mitigation strategy for deer-vehicle collisions. Wildl Biol. 2012;18(4):414–23. doi:10.2981/11-122.CrossRefGoogle Scholar
  111. 111.
    Baxter-Gilbert JH, Riley JL, Lesbarreres D, Litzgus JD. Mitigating reptile road mortality: fence failures compromise ecopassage effectiveness. Plos One. 2015;10(3). doi:10.1371/journal.pone.0120537.
  112. 112.
    Gagnon JW, Loberger CD, Sprague SC, Ogren KS, Boe SL, Schweinsburg RE. Cost-effective approach to reducing collisions with elk by fencing between existing highway structures. Human Wildl Interact. 2015;9(2):248–64.Google Scholar
  113. 113.
    Reses HE, Rabosky ARD, Wood RC. Nesting success amd barrier breaching: assessing the efffectiveness of roadway fencing in diamondback terraoins (Malaclemys terrapin). Herpetol Conserv Biol. 2015;10(1):161–79.Google Scholar
  114. 114.
    Polak T, Rhodes JR, Jones D, Possingham HP. Optimal planning for mitigating the impacts of roads on wildlife. J Appl Ecol. 2014;51(3):726–34. doi:10.1111/1365-2664.12243.CrossRefGoogle Scholar
  115. 115.
    Ascensao F, Grilo C, LaPoint S, Tracey J, Clevenger AP, Santos-Reis M. Inter-individual variability of stone marten behavioral responses to a highway. Plos One. 2014;9(7). doi:10.1371/journal.pone.0103544.
  116. 116.
    Jaarsma CF, van Langevelde F. Effects of scale and efficiency of rural traffic calming on safety, accessibility and wildlife. Transp Res D Transp Environ. 2011;16(7):486–91. doi:10.1016/j.trd.2011.05.009.CrossRefGoogle Scholar
  117. 117.
    Jones DN, Griffiths MR, Griffiths JR, Hacker JLF, Hacker JB. Implications of upgrading a minor forest road on traffic and road-kill in southeast Queensland. Australas J Environ Manag. 2014;21(4):429–40. doi:10.1080/14486563.2014.944590.CrossRefGoogle Scholar
  118. 118.
    Meisingset EL, Loe LE, Brekkum O, Mysterud A. Targeting mitigation efforts: the role of speed limit and road edge clearance for deer-vehicle collisions. J Wildl Manag. 2014;78(4):679–88. doi:10.1002/jwmg.712.CrossRefGoogle Scholar
  119. 119.
    Neumann W, Ericsson G, Dettki H, Bunnefeld N, Keuler NS, Helmers DP, et al. Difference in spatiotemporal patterns of wildlife road-crossings and wildlife-vehicle collisions. Biol Conserv. 2012;145(1):70–8. doi:10.1016/j.biocon.2011.10.011.CrossRefGoogle Scholar
  120. 120.
    McLaren AAD, Fahrig L, Waltho N. Movement of small mammals across divided highways with vegetated medians. Can J Zool Rev Can Zool. 2011;89(12):1214–22. doi:10.1139/z11-100.CrossRefGoogle Scholar
  121. 121.
    Kusta T, Keken Z, Jezek M, Kuta Z. Effectiveness and costs of odor repellents in wildlife-vehicle collisions: a case study in Central Bohemia, Czech Republic. Transp Res D Transp Environ. 2015;38:1–5. doi:10.1016/j.trd.2015.04.017.CrossRefGoogle Scholar
  122. 122.
    Zuberogoitia I, del Real J, Torres JJ, Rodriguez L, Alonso M, de Alba V, et al. Testing pole barriers as feasible mitigation measure to avoid bird vehicle collisions (BVC). Ecol Eng. 2015;83:144–51. doi:10.1016/j.ecoleng.2015.06.026.CrossRefGoogle Scholar
  123. 123.
    Grosman PD, Jaeger JAG, Biron PM, Dussault C, Ouellet J-P. Trade-off between road avoidance and attraction by roadside salt pools in moose: an agent-based model to assess measures for reducing moose-vehicle collisions. Ecol Model. 2011;222(8):1423–35. doi:10.1016/j.ecolmodel.2011.01.022.CrossRefGoogle Scholar
  124. 124.
    Chang Y-H, Wu B-Y, Lu H-L. Using ecological barriers for the conservation of frogs along roads. Ecol Eng. 2014;73:102–8. doi:10.1016/j.ecoleng.2014.09.009.CrossRefGoogle Scholar
  125. 125.
    van der Grift EA, van der Ree R, Fahrig L, Findlay S, Houlahan J, Jaeger JAG, et al. Evaluating the effectiveness of road mitigation measures. Biodivers Conserv. 2013;22(2):425–48. doi:10.1007/s10531-012-0421-0.CrossRefGoogle Scholar
  126. 126.
    D’Amico M, Clevenger AP, Roman J, Revilla E. General versus specific surveys: estimating the suitability of different road-crossing structures for small mammals. J Wildl Manag. 2015;79(5):854–60. doi:10.1002/jwmg.900.CrossRefGoogle Scholar
  127. 127.
    Rytwinski T, van der Ree R, Cunnington GM, Fahrig L, Findlay CS, Houlahan J, et al. Experimental study designs to improve the evaluation of road mitigation measures for wildlife. J Environ Manag. 2015;154:48–64. doi:10.1016/j.jenvman.2015.01.048.CrossRefGoogle Scholar
  128. 128.
    Collinson WJ, Parker DM, Bernard RTF, Reilly BK, Davies-Mostert HT. Wildlife road traffic accidents: a standardized protocol for counting flattened fauna. Ecol Evol. 2014;4(15):3060–71. doi:10.1002/ece3.1097.PubMedPubMedCentralCrossRefGoogle Scholar
  129. 129.
    Santos SM, Carvalho F, Mira A. How long do the dead survive on the road? Carcass persistence probability and implications for road-kill monitoring surveys. Plos One. 2011;6(9). doi:10.1371/journal.pone.0025383.
  130. 130.
    Santos SM, Marques JT, Lourenco A, Medinas D, Barbosa AM, Beja P, et al. Sampling effects on the identification of roadkill hotspots: implications for survey design. J Environ Manag. 2015;162:87–95. doi:10.1016/j.jenvman.2015.07.037.CrossRefGoogle Scholar
  131. 131.
    Beckmann C, Shine R. Do the numbers and locations of road-killed anuran carcasses accurately reflect impacts of vehicular traffic? J Wildl Manag. 2015;79(1):92–101. doi:10.1002/jwmg.806.CrossRefGoogle Scholar
  132. 132.
    Teixeira FZ, Coelho IP, Esperandio IB, Oliveira NR, Peter FP, Dornelles SS, et al. Are road-kill hotspots coincident among different vertebrate groups? Oecologia Australis. 2013;17(1):36–47.CrossRefGoogle Scholar
  133. 133.
    Teixeira FZ, Pfeifer Coelho AV, Esperandio IB, Kindel A. Vertebrate road mortality estimates: effects of sampling methods and carcass removal. Biol Conserv. 2013;157:317–23. doi:10.1016/j.biocon.2012.09.006.CrossRefGoogle Scholar
  134. 134.
    Stevens BS, Dennis B. Wildlife mortality from infrastructure collisions: statistical modeling of count data from carcass surveys. Ecology. 2013;94(9):2087–96. doi:10.1890/12-1052.1.PubMedCrossRefGoogle Scholar
  135. 135.
    Snow NP, Porter WF, Williams DM. Underreporting of wildlife-vehicle collisions does not hinder predictive models for large ungulates. Biol Conserv. 2015;181:44–53. doi:10.1016/j.biocon.2014.10.030.CrossRefGoogle Scholar
  136. 136.
    Grilo C, Ascensao F, Santos-Reis M, Bissonette JA. Do well-connected landscapes promote road-related mortality? Eur J Wildl Res. 2011;57(4):707–16. doi:10.1007/s10344-010-0478-6.CrossRefGoogle Scholar
  137. 137.
    Cureton II JC, Deaton R. Hot moments and hot spots: identifying factors explaining temporal and spatial variation in turtle road mortality. J Wildl Manag. 2012;76(5):1047–52. doi:10.1002/jwmg.320.CrossRefGoogle Scholar
  138. 138.
    Gunson KE, Ireland D, Schueler F. A tool to prioritize high-risk road mortality locations for wetland-forest herpetofauna in southern Ontario, Canada. Northwest J Zool. 2012;8(2):409–13.Google Scholar
  139. 139.
    Rytwinski T, Fahrig L. Why are some animal populations unaffected or positively affected by roads? Oecologia. 2013;173(3):1143–56. doi:10.1007/s00442-013-2684-x.PubMedCrossRefGoogle Scholar
  140. 140.
    Downs J, Horner M, Loraamm R, Anderson J, Kim H, Onorato D. Strategically locating wildlife crossing structures for Florida panthers using maximal covering approaches. Trans GIS. 2014;18(1):46–65. doi:10.1111/tgis.12005.CrossRefGoogle Scholar
  141. 141.
    Loro M, Ortega E, Arce RM, Geneletti D. Ecological connectivity analysis to reduce the barrier effect of roads. An innovative graph-theory approach to define wildlife corridors with multiple paths and without bottlenecks. Landsc Urban Plan. 2015;139:149–62. doi:10.1016/j.landurbplan.2015.03.006.CrossRefGoogle Scholar
  142. 142.
    Langen TA, Gunson KE, Scheiner CA, Boulerice JT. Road mortality in freshwater turtles: identifying causes of spatial patterns to optimize road planning and mitigation. Biodivers Conserv. 2012;21(12):3017–34. doi:10.1007/s10531-012-0352-9.CrossRefGoogle Scholar
  143. 143.
    Laporte M, Beaudry C-OS, Angers B. Effects of road proximity on genetic diversity and reproductive success of the painted turtle (Chrysemys picta). Conserv Genet. 2013;14(1):21–30. doi:10.1007/s10592-012-0419-x.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Geology, Energy and the EnvironmentTexas Christian UniversityFort WorthUSA

Personalised recommendations