A Review of Wildlife–Vehicle Collisions: A Multidisciplinary Path to Sustainable Transportation and Wildlife Protection

Abstract

This review synthesizes historical and contemporary research on wildlife–vehicle collisions and roadkill, outlining its evolution from early documentation to modern road ecology. It discusses how early efforts in North America and Europe that quantified animal casualties and developed standardized methodologies formed current studies that use advanced geospatial tools, citizen science, and artificial intelligence to analyze spatiotemporal patterns. We examine key ecological, methodological, and economic impacts of roadkill on wildlife populations and human safety, highlighting the role of road density, vehicle speed, and seasonal factors. The framework presented also underscores a commitment to sustainability by integrating environmental conservation with infrastructural development and socio-economic resilience. The review details various mitigation strategies, from fencing and wildlife crossings to dynamic signage, and evaluates their effectiveness in reducing mortality rates, thereby supporting sustainable development in transportation infrastructure and wildlife management. It also identifies research gaps and outlines future directions, advocating for integrated, multidisciplinary approaches to improve wildlife conservation, infrastructure planning, and public awareness in the context of rapidly expanding road networks.

1. Introduction

The earliest roadkill documentation in North America dates to the 1930s. In 1925, D. Stoner conducted the first effort-based, repeated count involving 29 reptile, bird, and mammal species [1]. A decade later, in 1934, W.H. Davis found one rabbit killed per mile of highway [2]. In 1936–1937, investigations in Iowa systematically recorded species, locations, and mileage, allowing quantified analyses [3]. T.G. Scott was the first to study carrion persistence [3], and his methods were later used in Michigan [4] and Nebraska [5].

In Europe, roadkill investigations started from birds [6], followed by a paper on various causes of animal deaths, including so-called “motor traffic” [7]. Similar historical research outside North America and Europe has been missing, with few earliest records from Asia, Africa, Australia, and South America emerging in the 1990s or later [8,9,10].

Early wildlife mortality studies evolved into systematic ecological research known as road ecology. In Sweden, structured observational studies began in the 1960s, quantifying species-specific mortalities and seasonal patterns [11]. In the Netherlands, a strong relationship was suggested between roadkill and traffic volume, influenced by sampling intensity and species population dynamics [12]. To maintain Scandinavian leadership, A. Seiler’s PhD thesis summarized many aspects of roadkill in Sweden [13]. In the US, road ecology development in 2015 was summarized by G. Kroll [14], while in 1998, R.T. Forman and L.E. Alexander already linked early roadkill insights to habitat connectivity, ecological barriers, and biodiversity conservation [15]. Later, A.W. Coffin published a review on road ecology [16]. The last two papers were foundational in road ecology but lack several key elements now considered essential. They have little global representation and do not include recent technologies like AI, drones, or citizen science. They also miss connections to sustainability goals, economic impacts, and transportation policy. Unlike modern reviews, they lack systematic methodology and overlook emerging topics such as disease surveillance through roadkill, scavenger ecology, and the cost-effectiveness of mitigation. These gaps underscore the need for updated, interdisciplinary approaches in road ecology research.

In recent decades, roadkill research has expanded globally with exponential growth of publication numbers [17]. It broadened to include influencing factors and mitigation measures. With geospatial tools, wildlife cameras, drones, and thermovision, investigations advanced further. Citizen science and mobile apps were also adopted, and the environmental, economic, and social consequences of roadkill were recognized.

The aim of the paper is to provide a comprehensive review of roadkill research by synthesizing historical developments and current knowledge on its impacts on wildlife and humans, examining roadkill mitigation strategies and technological innovations in road ecology, and identifying research gaps and future perspectives in the field. The paper underscores the importance of sustainability, illustrating how effective road management and wildlife protection can inform policies that foster long-term environmental integrity and socially responsible infrastructure development.

2. Materials and Methods

Published papers for this review were selected using a systematic and reproducible approach to ensure comprehensive coverage.

We defined the wide scope of this review to include the following: (1) the impact of roadkill on wildlife; (2) animal behavioral adaptations to roads; (3) spatiotemporal patterns in roadkill and related environmental factors; (4) the impact of roadkill on humans; (5) roadkill mitigation strategies and solutions; and (6) a discussion of research gaps and future directions in roadkill ecology. The timeframe for the analyzed sources is the last 2–3 decades, with a limited number of the older ones, the taxonomic scope includes mainly mammals, and the geographical focus was global.

Papers were searched in academic databases, mainly Google Scholar and Web of Science, including a limited number of findings from JSTOR. We used various combinations of search terms, mainly “roadkill”, “wildlife-vehicle collisions”, “roadkill and human safety”, “roadkill hotspots”, “road mortality”, “roadkill ecology”, “road ecology”, “roadkill mitigation”, “wildlife crossings”, “impacts of roads on wildlife”, “factors affecting roadkill”, related to the main scope of this review and chapters below. We used Boolean operators (AND, OR) to refine search results. When looking for case examples, country names were added to the used search terms.

As these searches yielded thousands of records in Google Scholar alone, findings were filtered, first including best-cited journal articles, conference proceedings, reports, and book chapters, as well as empirical studies, meta-analyses, and systematic reviews. Our initial target was not to exceed 300 references. Therefore, most publications on non-mammal vertebrates were excluded.

Screening of the sources for inclusion/exclusion was initially performed by removing obviously irrelevant papers based on title and abstract. As a second step, we obtained full texts and screened them to confirm relevance. We also used an AI-based search program, Undermind [18], to check if our findings are among the top recommendations for specific searches.

After a draft of the text was finished, we ended with more than 400 references; therefore, at the text polishing phase, the number of sources was diminished, excluding publications where full text was not available, most of the grey literature and studies in foreign languages if the translation was not available, and, finally, papers not providing original data (Figure 1).

We have opted to present only common species names in the main text and to provide the full list of species with their scientific names in Appendix A, listed in the order of first appearance.

3. Results

3.1. The Impact of Roadkill on Wildlife

Since the 1930s in North America and Europe [2,3,7], roadkill has been recognized as animals struck and killed by vehicles. With millions dying annually in wildlife–vehicle collisions (WVCs), this issue poses a global threat to wildlife and human safety and presents significant economic and conservation challenges [19]. Monitoring roadkill not only quantifies mortality but also supports analyses of population trends, species distribution, animal behavior, and disease surveillance [17].

3.1.1. Roadkill Incidences and Trends

As highways and roads continue to expand, millions of animals are killed in WVCs each year; however, not many publications present country-wide extrapolations. There is no standardized dataset available for direct long-term and cross-country comparisons in Europe spanning the last two decades. Some numbers of roadkill in European countries, covering different estimates of the 1965–2016 period, are presented by A.L.W. Schwartz et al. [17]. According to these authors, the estimated number of vertebrates killed on roads annually is 32 million in Germany, 10 million in Spain, 8.7 million in Sweden, 8.3 million in Denmark, 6.5 million in Finland, 5 million birds in Hungary, 4 million in Belgium, 2.7 million in Great Britain, and 2 million in the Netherlands. A multi-country study estimated that approximately 194 million birds and 29 million mammals are killed annually on European roads [20].

Despite consensus on rising roadkill numbers [20], trends are less documented. A 10-year European study [21] and Czech data [22] show a continuous rise in deer and ungulate collisions, with a 25% increase linked to growing deer densities. In Sweden, collisions climbed from 10,000 in 1982 to 55,000 by 1993 [23]; Belgium saw a 21% increase from 2003 to 2011 [24]; mammalian roadkill in Lithuania, especially roe deer, rose exponentially between 2007 and 2022 [25].

Other continents show similar trends. For instance, bi-weekly surveys on Phillip Island (Australia) recorded an increase in roadkill rates from 1.59 to 2.39 per km per month between the 1990s and 2014 [26]. In New Zealand, roadkill rates for possums, hedgehogs, and rabbits varied over six decades: possum collisions rose 80% from 1984 to 1994, then dropped 60% by 2005; hedgehog numbers surged in 1988–1989 before declining 82% between 1994 and 2005; and rabbit roadkill increased 59% from 1994 to 2005 despite the introduction of rabbit hemorrhagic disease in 1997 [27].

COVID-19 transport restrictions temporarily reduced roadkill [28], though not for all species [29]. For example, mountain lion mortality in California declined by 58% [30], marsupial deaths in Tasmania dropped by 48% [28], and hedgehog mortality in Poland fell by about 50% [31]. In a study of 11 countries, Sweden showed no significant change in large animal roadkill, while Estonia, Spain, Israel, and the Czech Republic experienced declines over 40% [32]. In Lithuania, collisions on major roads dropped 90% during lockdown [33], although urban areas saw increases. In the US, shelter-in-place orders reduced traffic by up to 70%, leading to a 34% overall decline in WVCs and potentially saving tens of millions of vertebrates in one month [30,34]. This phenomenon was part of the Anthropause, and the roadkill was just one of its multidimensional manifestations [34].

3.1.2. The Most Affected Species

In Europe, the most affected mammals are hedgehogs, badgers, foxes, and roe deer [20], while in the USA, they are white-tailed deer, raccoons, and opossums [35]. Patterns vary regionally: Northern Europe is dominated by roe deer [23], whereas Southern Europe sees a mix of roe deer and wild boar [36]. For example, in Spain and Croatia, roe deer and wild boar account for most wild ungulate collisions [37,38], with similar trends observed in the Czech Republic and Slovenia [39,40]. In Belgium, wild boar are the primary victims [34], and in Ireland, rabbits, hedgehogs, badgers, and foxes make up 78% of mammal roadkills [41]. In Great Britain, hedgehogs alone number 167–335 thousand roadkills annually [42]. Studies also suggest that small species are often under-sampled, though, in some regions, small mammals dominate roadkill counts [43,44,45]. These results contrast with the roadkill study in Lithuania, where rat-sized and smaller rodent and insectivore species comprised only 0.08% of roadkill [46].

3.1.3. Direct Impacts on Wildlife Populations

Roadkills reduce survival, effective population size, and gene flow, leading to unsustainable declines and genetic erosion, particularly in species with low reproductive rates or high mobility. Road mortality affects key parameters like sex- and age-biased mortality, annual roadkill percentage, its contribution to total mortality, and impacts during long-distance movements [47]. Species with low reproductive rates and large home ranges are especially vulnerable [20], and shifting roadkill hotspots can alter local population sizes [48]. Comparative analyses further underscore that large mammals with low reproductive rates experience more pronounced declines, emphasizing the need for targeted mitigation [20].

Concerning the largest African terrestrial mammals, on the Tsavo highway corridor, five African elephants and nine buffaloes were roadkilled in 12 years, raising conservation and safety concerns [45]. In Europe, 70 European bison were killed on roads and railways in Poland during 2010–2021, posing risks to both animals and people [49], and 6 were killed on Lithuanian roads during 2002–2017, this being a negligible threat to the species as a whole [46].

Roadkill impacts on ungulates and carnivores pose significant safety hazards [12]. In Europe, overabundant ungulates are increasing the number of wildlife–vehicle collisions [50], and similar issues are expected as carnivore numbers increase [51,52].

Higher moose density leads to more collisions [53]. Moose dominate Scandinavian roadkill [54,55], while red deer and other cervids are significant in Norway [56] and England [57], though their overall population impact appears minimal. In Lithuania, moose roadkill accounted for up to 28.6–78.8% of removals during low hunting pressure but has stabilized at around 10% [58]. For Finland’s wild forest reindeer, roadkill mainly affects adults, which is significant for conservation given their global population of about 5300 and low reproductive rates [59].

WVC effects on roe deer populations are rarely analyzed, though collisions are frequent. Roe deer account for 80% of all WVCs in Germany [60], 70% in Croatia [38], 55% in Estonia [61], and 40% in Spain [62]. In Lithuania, roe deer roadkill made up 16.5–16.6% of the species hunting bag in 2020–2021 [28].

Roadkill threatens large carnivores such as bears by contributing to population declines and reduction in genetic diversity, although the potential effects of roadkill on species’ trophic interactions are not well understood. Evidence from Europe [63,64] and North America [65] supports these effects. In Cantabria, Italy, roadkill-related brown bear mortality is rising as the population recovers [61]. Notably, bears do not depend on roadkill for food; rather, increased seasonal movement [64] and attraction to human-provided food near roads [63] heighten collision risks.

Road collisions contribute significantly to mortality in various large felids, including tigers, lions, Florida pumas, and Iberian lynx [8,65,66,67,68,69]. In Ohio, 6–18% of a recovering bobcat population is lost annually to vehicle collisions [70]. WVCs cause 40% of ocelot deaths in Texas, USA [71].

Regional studies document roadkill’s demographic and genetic impacts on smaller felids in Europe, South America, and Africa [72,73], though its overall magnitude remains unclear [68]. Cross-continental analyses linking mortality rates to long-term population changes are lacking [47,74]. In Europe, roadkill causes over 50% of wildcat deaths, with notable sex-specific survival biases on high-traffic roads [72]. In the Americas, roadkill hotspots for Southern tiger cat, margay, and jaguarundi correlate with road density in Brazil and Mexico [68,73], while African road patrols identify roadkill as a key ecological factor affecting serval mortality [75].

Data on roadkill for smaller mammals is scarce due to underreporting, leading investigators to depend on professional observations [46,76] and citizen science [77]. Although research in regions like Brazil is extensive, studies are often limited by short durations and small samples. Key areas of investigation include species composition [78,79], spatiotemporal patterns [80,81,82,83], and mitigation strategies [84].

The Eurasian otter has received notable attention in Europe, where it once served as a flagship conservation species. Despite its aquatic nature, WVCs are the leading cause of mortality [85]. In the UK, otter roadkill increased from 1971 to 1996 alongside population recovery [86]. In Uckermark County, Germany, 88 otters were recorded as roadkill between 1990 and 2003 [87], while in Lithuania, 22 were reported from 2002 to 2021, extrapolating to an estimated 154 (65–243) per year [88]. In some instances, roadkill may even regulate otter populations [89].

Hedgehog deaths from WVCs are an increasing conservation concern in Europe, with studies showing that roadkills may reduce local populations by around 30% [90,91]. Reported casualties vary widely: low rates of 0.001/km/year in Ireland [92] and 0.007/km/year in Finland [93] contrast with much higher rates in Spain, 0.76–1.42/km/year [94], Slovakia, 1.6/km/year [95], and Ukraine, 3.65/km/year [96]. In Lithuania, hedgehogs are the most frequently affected species in WVCs [46], while in Great Britain, road casualties have declined by up to 75% since 1990 despite remaining highest in urban–grassland interfaces [42]. Moreover, a large UK–Netherlands study reported that only 8.8% of 83,580 hedgehog deaths were due to roads, with illness (58.5%), poisoning, and injuries being the primary causes, whereas smaller studies in the UK and Finland found road mortality rates of 78% and 72.6%, respectively [93].

3.1.4. Endangered Species at Risk

Roads in tropical forests contribute not only to roadkills. In Southeast Asia, experts have identified 16 existing and 8 planned roads that potentially imperil about 21% of the region’s 117 endangered terrestrial mammal species by fragmenting habitats, accelerating deforestation, and facilitating illegal hunting pressures [97]. Another large-scale study in Brazil found that endangered mammals are also affected by vehicle collisions [98].

Vehicle collisions pose a major threat to Brazil’s threatened species. A review of 62 studies found that 61% of 102 IUCN-listed medium–large mammal species, including 14.5% of threatened species, are affected by roadkill—possibly totaling 8.7 million incidents annually [99]. Additionally, the average ratio of endangered to total roadkilled species is 0.34 [100]. Specific cases include the giant anteater, which loses approximately 20% of local individuals each year [101]; the potential extinction of tapir populations due to the loss of six individuals out of 126 over 38 years and a 10.4% annual roadkill rate for jaguars that could diminish their metapopulation by 35% over a century [102]; the maned wolf, a threatened Brazilian Savanna endemic [103]; and the thin-spined porcupine, now considered “Vulnerable to Extinction” partly due to vehicle collisions [104].

Among the particularly sensitive species threatened by roadkills [19], only one, the Iberian lynx, is classified as Endangered and is from Europe [67]. The other sensitive species, classified as Vulnerable (wild yak, African elephant, sun bear) or Endangered (Amur tiger, Asian elephant) and Critically Endangered Sumatran rhinoceros, are from Asia and Africa [19]. As reported by F. Lala et al. [45], Vulnerable African wild dog is also threatened by roadkills.

Endangered species in Japan are at significant risk from roadkill. A study on Iriomote cats reported 46 mostly fatal collisions over 31 years on a 50 km road [105]. Similarly, a 16-year study on the endangered Ryukyu long-furred rat documented 47 roadkill incidents alongside 75 live captures on northern Okinawa-jima Island [106]. In South Korea, escalated WVCs affect endangered leopard cats [107,108] and the yellow-throated marten [109].

3.1.5. Ecosystem-Wide Consequences

Road networks, while facilitating transportation, impose significant ecological consequences. Roadkill disrupts biodiversity by eliminating over 90% of trophic interactions in high-density areas of Europe [110] and fragmenting migration routes, thus affecting predator–prey dynamics and habitat connectivity [15]. Scavengers benefit from this subsidy, but apex predators suffer from prey depletion and mesopredator release, destabilizing ecosystems [111]. Moreover, localized trophic shifts occur due to altered mesocarnivore diets and competitive overlaps [112], affecting nearly 200 species, with basal species being more directly impacted than apex predators [110].

In parallel, roads also present physical barriers to movement, contributing to wildlife migration disruptions. Connectivity studies highlight how road infrastructure isolates populations, reducing genetic exchange and altering species distributions [113]. This inhibition of movement, when combined with roadkill-induced mortality, exacerbates habitat fragmentation and complicates conservation efforts [114]. Traffic intensity is very important, as after some threshold of intensity is exceeded, roads become non-permeable for animals [115].

Habitat quality and connectivity shape migration by defining movement corridors and crossing points [116]. Yet, high-quality habitats can also concentrate animal activity near roads, leading to increased roadkill. L. Balčiauskas et al. [117] showed that while habitat suitability models for ungulates can predict crossing zones, these areas often have higher roadkill rates. Integrating habitat models into road planning shows promise for mitigating roadkill, although animal behavior and environmental interactions complicate outcomes. For instance, R.C. Cerqueira et al. [118] found that predicted felid corridors often do not align with roadkill hotspots, and L. Frangini et al. [119] highlighted that enhanced connectivity could inadvertently raise road mortality in species like the golden jackal.

These findings emphasize the need for informed road infrastructure development and targeted conservation strategies to mitigate the negative impacts of roads and traffic and thereby preserve the integrity of ecological networks, identifying critical areas where road-induced cascade effects may be most pronounced and groups of species that may be at higher risk [110].

3.1.6. Species-Specific Vulnerability

Species-specific traits, encompassing behavioral attributes (e.g., boldness, movement patterns), morphological characteristics (e.g., body size), and ecological preferences (e.g., habitat use, migration), critically determine mammal roadkill vulnerability [83,120]. Moreover, predicted roadkill rates for Brazilian endotherms indicate that several understudied, IUCN-listed threatened species may be particularly vulnerable [121].

Mammals with varied diets exhibit different roadkill rates, with herbivores being less affected than omnivorous and insectivorous species, while factors like home range, body mass, and activity patterns are less significant [122]. Road-crossing behavior depends on an animal’s perception and mobility [123]; for example, nocturnal species may face higher risk as reduced nighttime traffic lowers the perceived threat [124], and species frequently exposed to hunting may avoid roads due to heightened awareness of human risks [125]. Additionally, agility, behavior, and visibility further influence collision likelihood [126]. Overall, fully understanding road mortality requires examining multiple ecological, behavioral, and life-history traits concurrently.

Species with scavenging behavior, early maturity, and habitat generalism suffer higher roadkill mortality. For Brazilian mammals, those with intermediate body masses (3–50 kg) and smaller home ranges are at higher risk, while larger mammals (>50 kg) experience lower rates, likely due to enhanced driver detection [121]. In Mediterranean habitats, high local abundance drives elevated road mortality, with common small mammals, primarily rodents, being hit more frequently than rare species [127]. Bat road mortality varies with behavior and landscape: bats that use open space aerial-hawking are more vulnerable, whereas cave-roosting bats tend to avoid brightly lit areas, and incidences decline with elevation due to shifts in species composition [128,129].

3.1.7. Secondary Ecological Effects: Scavengers and Disease Transmission

Roadkill not only reduces wildlife populations but also supplies a persistent food source for mammalian scavengers [130]. Studies indicate that scavengers like foxes, raccoons, coyotes, and hyenas rapidly remove carcasses—60–97% in Florida within 36 h [131] and 76% in UK urban areas within 12 h [132]. Changes in scavenger behavior with declining species diversity may trigger trophic shifts, emphasizing the need to conserve or reintroduce carnivores to maintain ecosystem balance since disrupting dominant scavengers can undermine vital ecosystem services [111].

Roadkill is used for pathogen detection and eco-epidemiological research, acting as a reservoir for agents such as Toxoplasma gondii [133] and zoonotic helminths [134]. S. Rohner et al. [135] identified various pathologies in Eurasian otters in Germany, highlighting disease prevalence and anthropogenic impacts. Urban road-killed mammals serve as indicators for tick-borne bacterial communities [136]. Additionally, J. Monsalve-Lara et al. [137] documented Mycobacterium leprae and M. lepromatosis in roadkilled Brazilian armadillos, while C. Calabuig et al. [138] assessed rabies and canine distemper viruses in wildlife from Northeastern Brazil, informing disease management and wildlife conservation.

3.1.8. Methodological Problems: Carcass Persistence and Misidentification

Data quality in citizen science roadkill projects is influenced by environmental conditions, collection methods, reporting tools, and participant expertise [139]. S.M. Santos et al. [140] found that carcass persistence varies by taxonomic group, most disappearing within a day, and is affected by body mass, road features, and weather, which dictate survey frequency. In addition, R. Barrientos et al. [141] reviewed 294 studies, highlighting that body mass is key to both searcher efficiency and carcass persistence and that trained dogs enhance detection rates. These findings underscore the importance of robust monitoring protocols to assess roadkill accurately and support conservation efforts.

Studies indicate that carcass persistence biases roadkill surveys, often leading to underestimation of mortality. Up to 89% of small carcasses are removed within 24 h, with rates varying by road type and time [142], depending on weather [143], while D.A. Henry et al. reported a median persistence of 2.7 days and a sharp decline in detection when survey intervals exceed three days [144]. Optimizing survey frequency based on these findings can reduce costs and improve mortality estimates.

Therefore, both the survey timing and frequency are crucial for accurately capturing roadkill counts. E. Guinard et al. [145] emphasize that detection rates vary significantly depending on when and how often surveys are conducted. For example, some studies implement surveys once or twice a month [45], while others use daily to weekly schedules [46] or even conduct surveys once a day [144]. In some cases, surveys were carried out up to four times per day [75], highlighting that more frequent monitoring can substantially improve detection accuracy.

Experts should verify the taxonomic identity of road-killed specimens collected by maintenance personnel [146]. While common and easily recognizable species are often identified correctly, rare or similar species (e.g., small wild canids and felids) are frequently misidentified, especially when multiple similar species co-occur. Targeted training, with the use of scaled images, and expert verification for selected species are recommended to enhance data reliability [147].

3.2. Animal Behavioral Adaptations to Roads

Animal road use serves five ecological functions: communication, foraging (e.g., anthropogenic food, herbivory, predation, salt, scavenging, water), movement (e.g., bridges, habitat connectivity), refuge (e.g., avoidance, burrowing, roosting), and thermoregulation, with foraging, movement, and refuge being most common [148]. Alternatively, roads can act as movement barriers, with an animal’s response depending on its movement abilities, proximity to the barrier, and habitat preferences [149]. The barrier effect varies by species due to differences in behavior, ecological needs, and road characteristics [150].

Behavioral adaptations to roads resemble responses to natural predators. Roads are generally avoided during high-traffic daytime, with crossings peaking at twilight and nighttime in food-rich open areas. Gregarious species tend to prioritize avoidance over speed, while ungulates often cross at twilight during winter commuter traffic [151]. Mammals exhibit both avoidance, driven by risk disturbance, and attraction behaviors linked to resource availability, especially in ungulates [152].

Animals adapt to roads by exhibiting avoidance behaviors and altering home ranges to reduce crossings [123,153]. Predator species such as foxes, bobcats, and coyotes learn to cross roads during periods of low traffic, increasing activity when human disturbance is minimal [154]. Similar behavioral adaptations in urban animal populations may help reduce roadkill risks in areas with high road densities [155].

3.3. Spatiotemporal Patterns and Environmental Determinants of Roadkill

Spatiotemporal patterns in roadkill have been widely studied, from local to global scales [156], so this review remains concise. Early studies in the 1990s–2000s highlighted roadkill as an ecological concern, emphasizing population structure and seasonality [12].

Regional spatial analyses expanded during 2010–2017 as specialized GIS tools became accessible in biodiverse areas like Brazil [157]. Advanced models such as boosted regression trees [158] replaced older software like SIRIEMA ver. 2.0 [159]. KDE and KDE+ now prevail in roadkill hotspot analysis across Europe [40,46,160,161,162], with KDE+ also used for mammalian habitat ranking [163].

Recent global data integrations via systematic reviews and meta-analyses reveal a bias toward developed countries [47], while studies in tropical biomes underscore high mammal vulnerability in areas of rich biodiversity [164]. Methodologies have evolved with space-time cubes [165] and trait-based models [121], and large citizen science datasets have highlighted dynamic “hotmoments” linking ecological impacts with urban safety and economic costs [57]. Significant gaps remain in global syntheses, underrepresented regions, and dynamic spatiotemporal integration [47].

Diurnal and seasonal trends consistently show roadkill peaks at dawn/dusk [24,57,115,127], during rainy or breeding seasons [127,166,167], and in migration or dispersal phases [47]. Shifts in species abundance and vehicle traffic are altering these spatiotemporal drivers, emphasizing the need to reassess mitigation measures [26,168].

Understanding the environmental determinants of roadkill is crucial for selecting effective mitigation measures and deciphering spatial patterns [16,80]. Studies have addressed these factors across regions: Europe [169]; North America [170,171]; South America, particularly Brazil [102,172]; Asia [107,173,174]; Africa [75]; and Australia [175].

Seasonal variations [169] and local differences [176] require regional, localized studies [177]. For example, in Lithuania, roadkill patterns for ungulates [117,178,179], semiaquatic species [180], and carnivores [181] are linked to distinct environmental factors. The lack of similar analyses in other regions represents a significant gap in road ecology.

3.4. The Impact of Roadkill on Humans

3.4.1. Wildlife–Vehicle Accidents and Human Casualties

Wildlife–vehicle collisions leading to human casualties are well documented as a rising global trend, with significant long-term datasets available for regions such as Europe and North America [24,182,183,184,185,186]. However, large data gaps persist in developing regions [187,188,189].

Studies indicate that while WVCs significantly threaten animal populations, they also pose serious risks to human safety. In the United States, animal-related vehicle crashes resulted in over 1300 human deaths in a decade, averaging approximately 165 deaths per year between 1995 and 2004, occurring mainly in rural areas on straight roads during clear weather, coinciding with increased animal activity and lack of occupant restraints [182,183]. In Wallonia, Belgium (2003–2011), 13% of WVCs led to injuries and less than 1% to fatalities, with incidents clustering around wild boar and roe deer [24]. In the Czech Republic, where roe deer and wild boar dominate WVC statistics, less than 2% of collisions resulted in injury; however, motorcyclists were up to 1600 times more likely than car occupants to be injured, especially during evasive maneuvers [185]. In Lithuania, wildlife–vehicle accidents showed an upward casualty trend from 2002 to 2022, with moose causing a disproportionate number of fatalities, mostly occurring at dusk or dawn in late spring and early fall on main roads and involving cars and motorcycles [186].

Research shows that in the United States, wildlife–vehicle collisions cause over 440 fatalities and 59,000 injuries annually, with many deaths resulting from secondary impacts rather than the animal collision itself [184,190]. In Saudi Arabia, camel–vehicle collisions result in fatalities four times higher than other crashes [191]. In eastern India, stray animals, especially dogs and cattle, significantly contribute to severe accidents involving young two-wheeler riders [187]; in Brazil, collisions with neotropical mammals lead to extensive traumatic injuries, underscoring the need for improved wildlife emergency care and forensic investigations [188]. Collectively, these studies highlight the pressing need for enhanced prevention strategies, awareness campaigns, and infrastructure adaptations worldwide.

Research on WVCs is extensive; however, only one study explicitly examines secondary crashes [192], with a few others indirectly addressing the issue [186,190]. Secondary effects, such as vehicles running off the road, colliding with trees or guardrails, or rolling over, significantly contribute to human injuries and fatalities. In Michigan, USA, for example, less than 4% of deer-related crashes resulted in direct human injury, with the majority of injuries stemming from secondary collisions [193].

3.4.2. Economic Costs

WVCs involving large mammals like elk, moose, or wild boar inflict significant damage on vehicles and infrastructure. In São Paulo State, Brazil, the average cost per animal–vehicle crash was about USD 9629, contributing to annual economic losses exceeding USD 25 million and over USD 1 million in road administrator compensation claims [194]. In Utah, USA (1996–2001), vehicle damage alone accounted for 39% of WVC-related costs, approximately USD 18 million, underscoring the financial burden on both vehicle owners and public agencies [195].

Medical expenses and insurance issues significantly add to the economic burden of WVCs. In Utah, USA, fatality costs constituted 53% of losses (around USD 24 million), and non-fatal injuries added an additional USD 1 million [195]. In Sweden, rising wild boar–vehicle collisions led to personal injury costs estimated at EUR 2222–2454 per case, culminating in an annual total of up to EUR 12.3 million [196]. Health care expenses include emergency services, hospitalization, surgeries, and long-term rehabilitation, while insurance complexities stem from differing WVC classifications affecting liability, deductibles, and premium adjustments. In Brazil, where legal frameworks often hold road operators liable for preventing animal access, these dynamics further shape insurance and compensation [194].

Road maintenance and mitigation costs are substantial. Routine efforts, such as carcass removal and repairs, require dedicated resources, while long-term investments like fencing and wildlife crossings, though expensive, have proven cost-effective [197]. For example, exclusion fencing along the Trans-Canada Highway reduced ungulate collisions by up to 96%, yielding a net economic benefit of over USD 500,000 per kilometer over 10 years [198]. In Washington State, USA, wildlife crossing structures are estimated to prevent approximately 0.6 to 1.9 collisions per kilometer, generating monetized benefits of USD 235,000–443,000 per structure each year [199].

Updated cost–benefit models now bolster the case for earlier mitigation, as rising vehicle repair, medical, and conservation costs justify installing fencing and crossings sooner than previously recommended. The analysis shows that once WVC rates exceed certain thresholds, mitigation becomes financially defensible [197]. These strategies enhance both human safety and wildlife conservation but require strategic investment and cross-sector collaboration over time.

3.4.3. Psychological and Emotional Effects

According to C. MacKay [200], roadkill exposes our shared vulnerability and underscores our ethical responsibility toward animals. Beyond the immediate physical injuries of wildlife–vehicle collisions (WVCs), significant psychological trauma may follow; for instance, collisions with large mammals can lead to severe injuries and are associated with notable psychological distress. Studies indicate that up to 25% of patients severely injured in large animal WVCs experience psychological reactions, and 3–6% develop long-term post-traumatic stress disorder [201], underscoring the need for integrated mental health interventions in post-collision care.

Beyond physical injuries, WVCs trigger strong emotional responses. In Tasmania, tourists reported feelings of sadness, anger, and disgust upon encountering roadkill—especially women and biodiversity-focused visitors [202]. Additionally, fatalities in WVCs intensify emotional distress by compounding grief, guilt, and fear, which can influence future driving behavior and overall emotional well-being [203].

A Brazilian study found that truck drivers who reported fear or disgust toward certain animals did not change risky driving behavior, which was instead guided by perceived danger to the driver, vehicle, or others. This indicates that emotional awareness alone is not enough to alter dangerous driving without structural interventions such as fencing or wildlife crossings [204]. Together, these findings underscore the complex interplay between emotional response, trauma, and decision-making in wildlife–vehicle collisions.

3.5. Roadkill Mitigation Strategies and Solutions

Mitigating wildlife–vehicle collisions requires a multifaceted strategy that combines infrastructure design, technological interventions, and behavioral approaches to enhance road safety for both humans and animals [205]. Effective measures include fencing with or without crossings, wildlife corridors, detection systems, reflectors, speed and volume reductions, and driver warning systems. T. Rytwinski et al. [206] demonstrate that fencing combined with crossing structures are the most effective, though costly, measures. Historical analyses by G. Kroll [14] show a trend toward “permeable highways” that integrate wildlife corridors to mitigate habitat fragmentation, and D. Lester [207] advocates for adaptive management strategies using baseline monitoring and targeted interventions such as signage, rumble strips, and vegetation clearance for significant roadkill reductions.

3.5.1. Measures Focused on Wildlife Protection

Road traffic kills hundreds of millions of animals annually, but a meta-analysis of 50 studies shows that mitigation measures can reduce roadkill by 40%. Fencing combined with crossing structures is most effective, reducing collisions by up to 83% for large mammals, whereas inexpensive measures like wildlife reflectors have little impact. More rigorous, long-term studies are needed to inform road planning decisions [206].

Wildlife exclusion fencing consistently reduces mammal roadkill by 54–96% [198], particularly for large ungulates [208]. Its effectiveness depends on species-specific design, addressing fence-end effects, and integration with crossings to overcome ecological barriers [206]. For instance, in Canada, high metal fences (2.4 m) completely eliminated moose–vehicle collisions and reduced moose presence within fenced areas by 95% over five years.

Effectiveness is lower for small and medium mammals due to inconsistent passage use and maintenance issues, with design gaps displacing rather than eliminating mortality [87,208,209,210]. Subsurface barriers are crucial for burrowing species [210]. Consequently, short or partial fencing is discouraged [209,211,212], and fence ends should be equipped with extensions and supplemental measures like electrified guards [213]. Additionally, designs must be adapted to local climates, for example, incorporating snow-proof features in cold regions and durable materials in arid zones [210].

Mitigation must be species-specific to account for different behavioral traits [91]. Ungulates benefit most from fences and crossings, while smaller mammals tend to bypass poorly designed systems [87,214]. Additional effects on both target and non-target mammal species are reviewed by D. Smith et al. [215].

Fencing is most effective when integrated with wildlife crossing structures. Long fencing segments (over 5 km) paired with crossings reduce large mammal-vehicle collisions by over 80%, while retrofitting underpasses with exclusion fencing increases usage by deer and bears and reduces collisions, yielding economic benefits over 25 years [211,216]. Therefore, fencing does not merely complement crossing structures; it is integral to their effectiveness.

Behavioral factors mandate species-sensitive planning for crossing structures, as species preferences, predator–prey dynamics, and human disturbance all affect usage. For instance, mule deer avoid underpasses frequented by mountain lions, while predators shift activity patterns to avoid humans [217].

Wildlife underpasses are used by various mammals across continents [94,216,218,219]. Although ungulates generally favor large underpasses [220], species like roe deer, moose, elk, and deer also use non-designated passages. In Sweden, roe deer and moose preferred non-wildlife underpasses wider than 11.5 m and taller than 5 m, particularly under low human use and varying forest cover conditions [221]. Similarly, Scandinavian research found no significant difference in crossing probability among wild boar, roe deer, and fallow deer between at-grade fauna passages (openings or gaps in fencing where wildlife can cross directly over the road surface) and wildlife-specific structures, suggesting that cost-effective at-grade options may help mitigate barriers along fenced roads [222].

In Arizona, long-term monitoring of six underpasses revealed a 72.4% crossing success rate for over 15,000 animals, with elk showing increased usage over time as they habituated to the structures, underscoring the importance of structure and placement [223]. A synthesis of 313 studies found that while only 14% evaluated actual changes in animal movement, 98% demonstrated some cross-road movement via these structures, with underpasses particularly effective in preventing movement decline in ungulates [224].

Mule deer prefer overpasses but use both types when accessible [220]. Wider overpasses (around 50 m) promote greater species diversity and crossing rates, especially when paired with appropriate fencing [225]. In fact, wildlife-dedicated crossings with fencing are 15.9 times more likely to be used by large carnivores, with natural materials and rounded designs further enhancing their success [226]. Proper fencing, whether complete or partial, is essential to guide animal movement and ensure safety on high-traffic roads [211,222].

Odor repellents offer a cost-effective, moderately successful tool for reducing WVCs, though effectiveness varies by species and context. In the Czech Republic, T. Kušta et al. [227] achieved a 37% reduction in animal mortality over two years, while a study across 18 road sections reported reductions of 26–43% [228]. More recent research found a 43–60% reduction in WVCs with olfactory repellents over two years, with the strongest impact during the first seven weeks, suggesting eventual habituation [229]. In Lithuania, a six-month trial using repellent Wam Porocol^®, based on isovaleric acid, at highway fence gaps reduced mammal crossings by 42% [230]. Overall, while promising in the short term, repellents should be viewed as supplementary measures, particularly during peak collision seasons or where fencing is incomplete or impractical.

Wildlife warning reflectors were designed to delay animal crossings by reflecting vehicle headlights at night, but their impact is limited. Their effectiveness is restricted to nighttime and is reduced at dawn and dusk when collisions are common [231]. While early studies reported short-term benefits [232], later reviews and meta-analyses found little to no lasting behavioral change in deer, with some studies even suggesting that reflectors might increase collision risk by startling animals [233,234,235].

Clearing roadside vegetation improves visibility and reduces collisions, especially near protected areas. F. Lala et al. [45] found higher roadkill in shrub-covered areas due to poor visibility and increased cover for wildlife, while open areas allow better detection. Meanwhile, vegetation can also serve as corridors for small mammals; Galantinho et al. [236] noted that wood mice favored road verges near firebreaks and taller shrubs, recommending vegetation management to support their movement. Conversely, for moose, roadside vegetation may attract foraging, increasing collision risk—prompting R.V. Rea et al. [237] to suggest early-season brush cutting to reduce the nutritional value of regrowth during high-risk periods.

Hunting effectively mitigates roadkill by reducing ungulate densities and lowering collision rates. For example, culling white-tailed deer in US urban areas decreased deer-vehicle collisions by 30–94%, particularly in high-risk zones [235,238], and managing moose populations correlates with reduced moose-vehicle collisions [53]. In France, higher annual hunting harvests, which serve as proxies for population size, were linked to increased collision densities for red deer, roe deer, and wild boar, underscoring the role of population control [239]. In Indiana, targeted recreational hunting over five years reduced collisions with deer by 21.12%, saving up to USD 1.26 million in damages and generating additional revenue through license sales [240]. Moreover, hunting influences animal behavior. Elk, for instance, exhibit road avoidance, reduced movement, and increased vigilance during hunting seasons—this is a “landscape of fear” effect that further reduces road crossings [241].

Multiple studies demonstrate that deer whistles are ineffective in preventing deer–vehicle collisions. P.M. Scheifele et al. [242] found that although some whistles produce sounds within white-tailed deer’s hearing range (2–6 kHz), their acoustic properties vary considerably, and their effectiveness remains unproven. J.H. Hedlund et al. [243] concluded that deer whistles are essentially useless, while L.L. Mastro et al. [244] and S.A. Valitzski et al. [245] reported that these devices fail to trigger flight responses or deter deer, indicating that auditory deterrents are unsuitable for collision prevention.

3.5.2. Measures Focused on Human Safety

Studies on road signage for reducing deer–vehicle collisions show inconsistent effects. T.M. Pojar et al. [246] found no difference in deer crossing-to-kill ratios with animated signs, while T.L. Sullivan et al. [247] reported a 50% reduction in collisions using temporary flashing-light signs. Both passive and active signs hold promise in certain contexts [243]; however, surveys by A.R.F. Bond and D.N. Jones [248] and evaluations by T. Rytwinski et al. [206] suggest that traditional warning signs often fail to produce lasting behavioral changes. Dynamic, animal-activated signs can modestly reduce vehicle speeds [249], yet reduced speed limits only yield minor impacts on collision rates [250]. Targeted deer advisory signs have shown short-term benefits [251], but their long-term efficacy remains uncertain.

Although meta-analyses find insufficient data to conclusively show that lowering speed limits reduces wildlife mortality [206,252], several studies support the role of speed reduction in roadkill mitigation. D. Lester [207] reported a 59% decrease in roadkill at trial sites with measures like driver signage and audible rumble strips. Similarly, D. Denneboom et al. [226] linked higher speed limits to increased wildlife mortality and recommended prioritizing speed reduction in collision hotspots. A.D. Pereira et al. [253] concluded that combining speed reduction measures, such as speed reducers, warning signs, cameras, and speed bumps, with interventions like fencing and wildlife passages effectively reduces mammal roadkills by allowing animals more time to avoid vehicles. Overall, the available evidence supports the idea that reducing vehicle speeds can be an effective component of a broader adaptive strategy to reduce road deaths.

3.5.3. Public Awareness and Driver Education Programs

Public awareness and driver education programs through various media aim to alert drivers to the risks of animal-vehicle collisions, especially during high-risk periods [235]. In Northern Tanzania, field surveys revealed that drivers perceive mammals as primary victims, often attributing collisions to high speeds and poor nighttime visibility, while only 35% had attended an education program [254]. Furthermore, lifetime collision exposure is higher among male drivers and those covering longer distances, with many drivers favoring physical mitigation measures such as fences and wildlife crossings [255]. In Brazil, traffic campaigns rarely address wildlife collisions, mostly focusing on domestic animals, indicating a need for greater wildlife inclusion [256]. Targeted wildlife-warning signage, such as a snake sign placed 100 m before a hazard, has been shown to change driver behavior and reduce collisions in protected areas [75]. However, a study from a peri-urban reserve in Australia found that although residents claimed to drive more cautiously at night, actual speeds did not corroborate these claims [257]. Therefore, many drivers (particularly high-risk groups like middle-aged males) lack adequate knowledge to avoid collisions, highlighting the importance of targeted educational campaigns to mitigate the social and economic costs of roadkill [258]. Although most driver education programs cover general road safety and hazard awareness, including some mention of wildlife risks, few countries require a dedicated module on roadkill avoidance as part of driver licensing, even in regions with substantial wildlife presence [205].

3.5.4. Technology and Future Innovations

Animal detection and dynamic signage systems can notably reduce roadkill and wildlife–vehicle collisions. T. Rytwinski et al. [206] report a 57% reduction for large mammals using animal detection systems—a moderate outcome compared to an 83% reduction achieved with fencing and crossing structures and significantly better than the mere 1% reduction from inexpensive wildlife reflectors.

Dynamic systems integrate sensors based on heat, seismic activity, or laser/infrared beam interruption to trigger roadside warning signs that often include speed detection. In Europe and North America, such systems have reported collision reductions ranging from 33% to 97%, with infrared-activated systems in Switzerland achieving nearly 80% reduction [235,259,260]. Additionally, several studies confirm that these systems tend to slow down drivers, further reducing collision frequency and severity [261,262,263].

Interactive or dynamic signage that responds to speeding traffic or intermittently displays messages has been shown to lower traffic speeds, with temporary signs proving more effective than permanent installations [264]. A variant under trial in Switzerland actively detects nearby animals to trigger blinking lights on roadside pillars and collects activation data for further analysis [265,266].

Highway lighting may reduce deer-vehicle collisions by 57–68%, though it remains unclear if this is due to improved driver visibility or animals avoiding lighted areas [267]. Clock time policies also influence collision rates: collisions are 14 times more frequent two hours after sunset, and the autumn switch to standard time increases collisions by 16% [268]. The model used by the cited authors [268] suggests that adopting year-round daylight saving time could save thousands of deer, reduce human fatalities and injuries, and save USD 1.19 billion annually, while permanent standard time might result in even higher costs. D. Lester [207] proposes using light-colored pavement to improve animal visibility at night, yet artificial lighting, although extending driver hazard detection [269], is overall associated with increased roadkill for most species, indicating complex species-specific responses [156,171].

Integrating GPS data with sensor-based tracking creates an effective early warning system for reducing roadkill [270]. GPS pinpoints collision hotspots, guiding the placement of virtual fences and alert systems [271], while sensor technologies, such as buried cable detectors and opto-acoustical devices, offer real-time alerts to drivers and wildlife managers. Mobile reporting further enhances data accuracy and supports long-term planning [272], and meta-analyses confirm that these combined approaches significantly reduce wildlife–vehicle collisions [206].

Recent research uses AI and machine learning to predict roadkill and improve vehicle safety [273]. For instance, thermal imaging combined with computer vision can detect deer at night with about 91% accuracy [274]. Other studies use generative models to handle limited crash data and enhance real-time risk predictions [275], while traditional methods like Random Forest and Gaussian Naïve Bayes forecast collision risks using environmental and time factors [276]. Geographic information systems and multispectral imagery further help map collision hotspots across extensive areas [277,278]. Overall, combining thermal imaging with deep learning for animal detection alongside data augmentation and classical ML methods for risk prediction appears promising for reducing roadkill and enhancing both traffic safety and wildlife conservation [279].

In-vehicle vision systems can help detect animals, primarily large mammals, near roads [235]. For example, Swedish vehicle-mounted night vision systems have already been adopted by several car manufacturers [280,281]. Additionally, a sensor system is under development to detect living beings unexpectedly crossing roads, enabling timely preventive measures. This study highlights the need to understand spatial and seasonal hotspots, as well as traffic and infrastructure factors, for designing effective protective systems on rural roads [282]. However, the reliability of these systems has not yet been reported.

Autonomous vehicles (AVs) can potentially reduce collisions by harnessing advanced sensor fusion, AI-based decision-making, and rapid hazard detection. P. Goudarzi and B.Hassanzadeh [283] review techniques such as image processing, deep learning, and short-range communication that enable AVs to predict and avoid collisions in real time. Similarly, M. Abdel-Aty and S. Ding [284] show through a matched case-control analysis that AVs with advanced systems generally have fewer accidents than human-driven vehicles, though challenges remain in low-visibility conditions and turning maneuvers. Meanwhile, P. Salvini et al. [285] argue that realizing the road safety benefits of self-driving cars depends on overcoming significant operational and legal challenges, and T. Miller et al. [286] emphasize that despite the promise of improved navigation through AI, current systems still face substantial safety risks due to algorithmic limitations.

Emerging technologies are further boosting AV safety capabilities. For example, H. Guo et al. [287] introduce a Wi-Fi-based detection system installed inside vehicles that swiftly identifies humans and animals and thus prevents collisions. S. Abaddi [288] combines quantum computing with GPT-4o to specifically tackle camel–vehicle collisions, demonstrating how interdisciplinary innovations can address unique AV safety challenges. Together, these studies illustrate that while AVs hold substantial potential to reduce collisions by detecting hazards more quickly and accurately than human drivers, ensuring their safe operation requires overcoming technical and regulatory challenges through continuous innovation and robust design. However, none of these technologies have yet been analyzed with respect to wildlife–vehicle accidents.

Autonomous vehicles have the potential to reduce WVCs, but this benefit depends on the ethical and technical priorities of their design. Current frameworks call for integrating animal detection into AV decision-making systems, yet challenges remain in ensuring protection for a broad range of species, especially smaller or less conspicuous ones, which may be excluded due to algorithmic bias or cost constraints [289,290]. Without proactive consideration of wildlife in AV development, there is a risk that these vehicles will replicate or even reinforce harmful human driving behaviors toward animals.

4. Discussion: Research Gaps and Future Directions in Road Ecology

4.1. Data and Methodological Gaps

Unified, standardized, and open-access datasets for long-term, cross-country comparisons, particularly in Europe, are limited, which hinders the analysis of trends over decades. The datasets mentioned as examples are not directly compatible with each other (

Table 1. Examples of roadkill databases and datasets. Abbreviations: OA—open access (Y—yes, N—no, P—permission required); A—amphibians, R—reptiles, B—birds, M—mammals, G—global, R—regional, C—country limited, N—sample size = number of records.

Name	OA	Coverage	N	Source
Global Roadkill Data	Y	ARBM; G; 1974–2024	208,570	[291]
Brazil Road-kill	Y	ARBM; R; 1988–2017	21,512	[98]
Srazenazver.cz	Y	MB; C; 2014–2025	168,026	[39]
Global Primate Roadkill Database	P	M; G; 1987–2023	2862	[292]
SPOTTERON Roadkill	Y	ARBM; G; 2014–2020	15,198	[293]
REMFA Equador roadkill	Y	ARBM; C; 2008–2022	5010	[294]
Animals under wheels	Y	ARBM; C; 1960–2020	89,276	[77]
CROS and MAWRW	Y	ARBM; R; 2009–2014	33,700	[295]
Lithuanian roadkill	N	M(ARB); C; 2002–2022	50,681	[296]

). The development of common monitoring protocols and centralized databases will enhance the accuracy of cross-regional analyses [17,47].

Carcass misidentification and persistence can bias mortality estimates, underscoring the need for optimized survey frequencies and improved detection methods [140,144]. Furthermore, developing cost-effective methodologies, such as those incorporating AI-assisted and automated detection, remains a clear research priority [141].

Understanding of roadkill dynamics is limited by insufficient integration of spatiotemporal trends. Advanced models like space-time cubes can be combined with long-term monitoring to address this gap [47,160,162,165].

Climate change is projected to intensify WVC risks by shifting migration patterns, expanding species ranges, and altering breeding behaviors, though research in this area has largely focused on snakes [297]. Current static mitigation measures, like fixed wildlife crossings, are inadequate for these dynamic changes. Key questions include how range shifts will create new WVC risks, whether existing structures can adapt, and which species will face greater long-term risks. Future research is expected to integrate climate projections into hotspot models, develop adaptive measures such as movable fences or multi-species crossings, and expand ecological monitoring to track climate-sensitive behavioral changes [19,102,298].

4.2. Taxonomic and Geographic Biases

Research on roadkill disproportionately focuses on large mammals and charismatic species, leaving smaller and less-studied species underreported [46,77]. There is also a spatial bias, with most studies conducted in developed regions, while data from Asia, Africa, South America, and tropical biomes are limited. Studies from Brazil [157] and tropical reviews [164] underscore the need to expand geographic scope and include diverse ecosystems and biodiversity hotspots [19].

4.3. Underestimation of Ecological and Socioeconomic Roadkill Impacts

The ecosystem-wide consequences of roadkill remain largely unexplored, though cascading effects on trophic networks, genetic diversity, and habitat connectivity are acknowledged [15,110,111]. Additionally, changes in species behavior near roads may further alter ecosystem functioning [126].

While economic and human safety costs of wildlife–vehicle collisions have been documented [30,35], future research is expected to explore the psychological, emotional, and community-level impacts of roadkill. This includes examining long-term trauma, refining cost–benefit analyses of mitigation strategies [184,299], and addressing the critical question of how low-cost, culturally appropriate roadkill mitigation can be implemented in underrepresented areas.

Most driver education programs include general road safety and hazard awareness with some mention of wildlife risk, but few require a dedicated roadkill avoidance module before licensing. The EnVeROS report [205] notes that in regions with significant wildlife presence—such as Scandinavia, North America, and Australia—guidance for navigating frequent wildlife crossings is typically part of broader highway safety training or road safety campaigns rather than a standalone component. This gap presents an opportunity to incorporate ecological safety and wildlife conservation into driver training, potentially reducing wildlife–vehicle collisions and their socioeconomic impacts.

In summary, driver education programs can incorporate emotional and ethical dimensions of wildlife–vehicle collisions by highlighting psychological impacts and promoting a sense of responsibility toward animals. Public policy may include targeted awareness campaigns and introduce wildlife hazard modules into licensing processes, particularly in high-risk areas. Providing access to mental health support for drivers involved in severe collisions can address the often-overlooked emotional consequences.

4.4. Complex Evaluation of Mitigation and Use of Technological Innovations

There is mixed evidence regarding the effectiveness of mitigation strategies, such as wildlife reflectors, repellents, and various fencing designs [40,300]. Future studies evaluating these methods in diverse contexts and over longer periods will determine those most effectively reducing roadkill while avoiding unintended ecological impacts. In addition to findings by T. Rytwinski et al. [206] and M. Bíl et al. [205], research by D. Lester [207] and A.R. Rendall et al. [26] underscores the need for comprehensive, context-specific evaluations.

Advancements in geospatial tools, such as drones, mobile apps, thermal imaging, and artificial intelligence, offer new opportunities for monitoring and mitigating roadkill. Future research will incorporate these technologies to enhance data collection accuracy and enable real-time management of roadkill hotspots [77,80].

In summary, addressing these gaps requires a multidisciplinary approach that enhances standardized data collection, expands geographic and taxonomic coverage, integrates advanced analytical techniques, and rigorously evaluates mitigation measures. This comprehensive strategy is essential to reduce the ecological, economic, and social impacts of roadkill.

4.5. Integrating Wildlife Conservation with Road Planning

Integrating wildlife conservation with road planning is essential to reduce habitat fragmentation and wildlife mortality. Roads divide landscapes and disrupt natural movement corridors, contributing to population declines and biodiversity loss [13,15]. Recent studies indicate that incorporating wildlife crossings, exclusion fencing, and adaptive road designs can help create “permeable highways” that allow safe animal movement while maintaining efficient transportation networks [14,17]. Additionally, advanced geospatial tools and hotspot analyses enable planners to pinpoint critical areas for conservation interventions, ensuring that road infrastructure supports both human and ecological needs [16,19]. This integrated approach is vital for sustainable infrastructure development in increasingly road-dominated landscapes.

Citizen science platforms and remote sensing offer cost-effective solutions for collecting roadkill data in regions with limited traditional monitoring. Citizen science initiatives have successfully engaged local communities to document roadkill events, yielding valuable datasets at minimal expense [77]. Meanwhile, remote sensing provides broad-scale, landscape-level monitoring and temporal analyses that help identify roadkill hotspots and trends.

In conclusion, future road ecology research is expected to adopt a multidisciplinary approach to address animal–vehicle collisions. Long-term ecological studies are critical for understanding impacts on populations, biodiversity, and genetics, while standardized, habitat-scale analyses can clarify collision risk factors. Targeted, species- and context-specific evaluations of mitigation measures are essential. Integrating citizen science with emerging technologies, such as AI, drones, and remote sensing, is anticipated to enhance detection and predictive modeling capabilities. Additionally, developing models that incorporate the effects of climate change and urbanization is vital for creating adaptive, future-proof mitigation strategies. Moreover, embedding sustainability principles into research efforts will help ensure that infrastructure development not only minimizes environmental impacts but also promotes the long-term health and resilience of both natural ecosystems and human communities.

However, the implementation of these technologies in rural or developing regions can face barriers, including high installation and maintenance costs, limited technical infrastructure, and inadequate data networks. These constraints highlight the need for low-cost, context-specific adaptations and greater investment in capacity building.

Therefore, governments, scientists, and conservationists must collaborate on a global scale, sharing policies, resources, and binding agreements, to develop adaptive, local solutions that reduce the extent of the wildlife–vehicle collisions. However, cross-sector cooperation remains difficult due to differing priorities, communication barriers, institutional differences, funding constraints, conflicting short- and long-term goals, and complex regulations.