Major roads drive higher raptor mortality: insights from long-term rehabilitation data

Major roads drive higher raptor mortality: insights from long-term rehabilitation data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Major roads drive higher raptor mortality: insights from long-term rehabilitation data Richard Laurie, Rachel L. White, Connor T. Panter This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8997953/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Road expansion represents a growing threat to biodiversity, particularly within urban and peri-urban landscapes where wildlife movements intersect with transportation networks. Raptors are especially vulnerable to vehicle collisions due to their hunting behaviour and use of roadside habitats, yet empirical evidence from the UK remains limited. Here, we use admissions data from wildlife rehabilitation centres (WRCs) in England and Wales to examine patterns and outcomes of raptor-vehicle collisions. We analysed 586 geo-referenced admissions involving 10 raptor species between 2001–2019, assessing how species, road type (minor vs. major), season, landscape type and age influence mortality outcomes. Most collisions occurred on minor roads, however, collisions on major roads were substantially more lethal. Raptors struck on major roads had approximately 2.7 times higher odds of mortality than those struck on minor roads, highlighting an important distinction between collision frequency and collision severity. Relative to their breeding population sizes, Tawny Owl ( Strix aluco ), Western Barn Owl ( Tyto alba ) and Common Buzzard ( Buteo buteo ) were disproportionately more likely to be admitted to WRCs following collisions with road vehicles than expected by chance. Raptor-vehicle collisions remained relatively stable, highlighting the persistent threat that roadways pose for raptors over time. This study provides the first multispecies assessment of raptor-vehicle collisions across road networks in the UK. Our findings emphasise the disproportionate impact of major roads on raptor survival and highlight the need for targeted mitigation actions, including verge management and strategic speed reductions, within urban and peri-urban ecosystems. birds of prey threats urbanisation raptors road ecology Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Global urbanisation and human population growth have accelerated the development of transportation infrastructure, resulting in the proliferation of roads and vehicles across landscapes (Ibisch et al. 2016 ). Currently the global road network spans over 21.6 million kilometres and is projected to grow by 14–23% by 2050, making road expansion one of the most urgent challenges for conservation biologists (Meijer et al. 2018 ). In the UK alone, roads cover ~ 398,900 km, and by 2021, 40.3 million people were registered as private car owners (Department of Transport 2021a, 2021b), highlighting the scale, density and dependence on road infrastructure and its potential impact on wildlife across nearly all terrestrial habitats. Road expansion contributes to habitat loss, fragmentation and edge effects, pollution, and alters wildlife movement patterns and behaviour (Forman and Alexander 1998 ; Coffin 2007 ; Cooke et al. 2020 ). One of the most pronounced direct effects includes wildlife-vehicle collisions, which are recognised as a major driver of vertebrate mortality worldwide (Gunson et al. 2011 ; Pagany 2020 ). Unlike other forms of anthropogenic mortality, vehicle collisions have an indiscriminate impact on wildlife regardless of demography, physical condition, or conservation status (Hill et al. 2019 ). Yet, studies suggest that actual wildlife-vehicle collision mortality rates may be significantly underreported due to carcass removal via scavengers and detection bias (Hill et al. 2019 ). Birds tend to be underrepresented in wildlife-vehicle collisions research, with research efforts concentrating on large mammals due to risks towards human safety (Pynn and Pynn 2004 ). Comparatively little is known regarding the effects of road vehicles on birds, which often interact with human infrastructure including roads (Bullock et al. 2024 ; Panter et al. 2026 ). Avian vulnerability to vehicle collisions is influenced by a range of interacting factors, including species-specific flight behaviour, body size, sensory limitations, traffic volume and speed, and the spatial configuration of roads relative to habitats and food resources (Santos et al. 2016 ). Species that forage or hunt along road verges, scavenge carrion, or regularly cross roads at low flight heights are particularly susceptible (Husby 2016 ). Within this broader context, raptors are disproportionately affected by road–vehicle collisions due to their attraction to roadside prey and carrion, low-altitude hunting strategies, and delayed take-off responses (Hager 2009 ; Bullock et al. 2024 ). Raptors, i.e. , Accipitriformes, Cathartiformes, Falconiformes, Strigiformes and Cariamiformes (McClure et al. 2019 ), are valuable indicators of ecosystem health (Rodríguez-Estrella et al. 1998 ; Donázar et al. 2016 ). As apex predators, raptors influence multiple trophic levels, and population declines can initiate trophic cascades with long-lasting effects transcending entire food webs (O’Bryan et al. 2022 ). Threats to raptors include direct and indirect persecution (Buij et al. 2025 ), climate change (Martínez-Ruiz et al. 2023 ), collisions with energy infrastructure (Smith and Dwyer 2016 ; Kolnegari et al. 2021 ), and other elements associated with urban landscapes such as collisions with road vehicles (Bullock et al. 2024 ). A recent global systematic review of causes of raptor morbidity and mortality highlighted the impact of the urban environment and human infrastructure on raptors (see Panter et al. 2026 ). Consequently, vehicle collisions are recognised as one of the leading causes of raptor mortality globally (Hager 2009 ; Dwyer et al. 2018 ; Boal and Dykstra 2018 ). Raptor species’ ecology may explain propensity to be hit by road vehicles. Scavenging raptors may be particularly susceptible to vehicle collisions due to their attraction to roadside carrion (Cieśluk et al. 2024 ). Scavengers feed opportunistically, and the presence of anthropogenic food sources, including roadkill, can significantly influence their behaviour and increase time spent near roads (Schwartz et al. 2018 ). Furthermore, previous research found that owls may be disproportionately susceptible to road–vehicle collisions (Panter et al. 2022 ). Owl-vehicle collisions may be exacerbated by their low foraging flight heights, blinding by vehicles at night, and difficulty detecting prey in noisy environments (De Jong et al. 2018 ). Road-specific features, including maximum speed limits and proximity to prey-rich habitats such as grasslands, may further influence collision probabilities (Hanmer and Robinson 2021 ). Similarly, agricultural intensification and habitat degradation are displacing both raptors and their prey toward roadside environments, increasing exposure and collision risks (Butet et al. 2010 ). The potential threat of raptor-vehicle collisions is particularly pronounced in developed countries, such as the UK, where over 70% of the natural landscape lies within 700 metres of a road (Cooke et al. 2020 ). Understanding the environmental and anthropogenic predictors of these collisions is essential for developing targeted national conservation and mitigation strategies. Given ongoing gaps in understanding the drivers of raptor-vehicle collisions, there is a need for research that links species, road characteristics, seasonality, and surrounding habitat types at the landscape level to guide targeted mitigation strategies across the UK. Here, we use admissions data from wildlife rehabilitation centres (WRCs) in southwest England and Wales to investigate patterns of raptor-vehicle collisions over a 19-year period (2001–2019). Specifically, we examine how 1) species, 2) road type (minor vs. major), 3) season, 4) surrounding landscape type, and 5) age class influence raptor mortality outcomes following collisions. This work builds on Panter et al. ( 2022 ) by leveraging WRC admissions data to examine how taxonomic, temporal and environmental characteristics influence raptor-vehicle collisions and their associated mortality. WRC admission records provide a valuable source of information on the primary causes of morbidity and mortality in wild raptor populations and have been widely used to assess anthropogenic threats to birds of prey (Morishita et al. 1998 ; Wendell et al. 2002 ; Molina-López et al. 2011 ; Thompson et al. 2013 ; Panter et al. 2022 , 2026 ). Building on previous findings, we hypothesised that scavenging species, such as Common Buzzard ( Buteo buteo ), would be overrepresented in collision admissions due to the consumption of carrion along roadways (Schwartz et al. 2018 ; Slater et al. 2022 ). We further expected mortality probability following collisions to vary with road type, landscape type, season, and age class. Collisions on major roads were predicted to result in higher mortality due to greater vehicle speeds and traffic volumes, increasing impact severity and reducing opportunities for avoidance (DeVault et al. 2014 ; Bullock et al. 2024 ). We also anticipated higher mortality risk in open landscapes (Hanmer and Robinson 2021 ), where raptors foraging or commuting at low altitude may be more exposed to traffic and have fewer visual or structural cues to facilitate avoidance compared with more structurally complex habitats such as woodland or hedgerow networks. We also expect to find seasonal variation in mortality (Conard and Gipson 2006 ), with higher mortality during winter and autumn when reduced daylight, adverse weather conditions, and lower prey availability may increase foraging along roads and reduce flight performance or reaction times. Finally, we predicted that juvenile birds would exhibit higher mortality than adults following collisions, reflecting lower flight experience, reduced hazard perception, and potentially poorer escape responses during high-risk encounters with vehicles (Orozco-Valor et al. 2024 ). METHODS Study region Admissions data were sourced from four WRCs, namely: Cuan Wildlife Rescue in Shropshire, England (lat, long: 52.590, − 2.573), Gower Bird Hospital in Glamorgan, Wales (51.580, − 4.099), Secret World Wildlife Rescue in Somerset, England (51.206, − 2.964), and Wild Wings Birds of Prey in Cheshire, England (53.444, − 2.522) (Fig. 1 ) (Panter et al. 2022 ). The study region spans a heterogeneous landscape characterised by a mosaic of rural, agricultural and peri-urban environments. Much of the region is dominated by low-intensity farmland, pastoral landscapes and upland areas with relatively low human population densities, interspersed with urban centres and transport infrastructure associated with towns and cities such as Bristol, Cardiff, Swansea, Shrewsbury and Chester. Road networks range from minor rural roads to major A-roads and motorways, providing broad representation of traffic environments across gradients of urbanisation and human activity. Admissions data Admissions data were sourced from Panter et al. ( 2022 ), which presents case-by-case records of raptors admitted to WRCs in southwest/west England and Wales between 2001–2019. For each record, the admitted bird was identified to species level, and the following information was extracted where available: 1) age (adult, juvenile, or unknown), 2) admission date (dd/mm/yyyy), 3) fate (released, captivity, or died), and, where possible, 4) location of the collision (latitude and longitude). Because only a single bird remained in captivity post-admission, this case was pooled with birds that were released and reclassified under a new factor level: “survived.” Raptor ages were not precise and were determined at the point of admission by WRC staff and veterinarians. To account for uncertainty in identifying fledging, juvenile, and immature individuals, ages were standardised into broad categories for analysis ( i.e. , “adult,” “juvenile,” and “unknown”). Where precise coordinates were unavailable, we georeferenced each admission record using descriptive qualitative data available alongside the individual record which was often provided by members of the public that discovered the bird, e.g. , “the bird was found on the road in front of St Andrews church”. In cases where information provided was too limited to accurately geo-reference the admission record, we omitted those records from the dataset. Using the admission dates, we created a new categorical variable, “season,” and classified each record into four groups: “spring” (March–May), “summer” (June–August), “autumn” (September–November), and “winter” (December–February). Road data Georeferenced raptor-vehicle collision sites were manually cross-checked against Ordnance Survey (OS) mapping layers via EDINA Digimap (EDINA Digimap 2025 ). Original road classifications comprised “Motorway,” “Primary,” “A roads,” “B roads,” “Local,” and “Minor” roads. For analytical purposes, we regrouped these into two broader categories: 1) “major roads” (which included motorways/freeways, primary roads, and A roads) and 2) “minor roads” (including B roads, local roads, and minor roads). In the UK, A roads are major through-routes connecting towns and cities, while B roads are smaller, secondary routes connecting local areas and communities. Landscape type data We downloaded land cover data at 25-metre resolution from the UKCEH’s Land Cover Map 2015 dataset (Rowland et al. 2017 ). The 2015 land cover dataset was used to coincide with approximate temporal coverage of the admissions dataset. We computed circular buffers (500 metre radii) around each georeferenced record, to account for potential uncertainty in exact collision locations, and extracted the proportionally dominant land cover type within each buffer using the zonal statistics feature within QGIS version 3.38 (QGIS Development Team 2025 ). From there, we reclassified the 21 local-scale habitat types into three broad landscape categories: 1) “open” ( i.e. , estuaries, moors and heathland, natural grasslands, non-irrigated arable land, pastures, and water bodies), 2) “closed” ( i.e. , coniferous forest, broad-leaved forest, transitional woodland-shrub, and mixed forest), and 3) “urban” ( i.e. , airports, construction sites, continuous and discontinuous urban fabrics, green urban areas, industrial/commercial, mineral extraction sites, ports, road/rail networks, and sport and leisure facilities). Estimating proportional representativeness within the dataset To quantify whether a particular species was over- or underrepresented in our dataset, we explored the relationship between relative population size for each species throughout the UK and the relative proportion of vehicle collision records from the four WRCs. Species population estimates were manually extracted from the British Trust for Ornithology’s BirdFacts database ( https://www.bto.org/learn/about-birds/birdfacts ). We extracted the population estimates as number of breeding pairs and multiplied this by two to estimate number of breeding individuals (this estimate does not include non-breeding individuals). Where ranged population estimates were provided, e.g. , 1,500–2,500 breeding pairs, we used the higher estimate, i.e. , 5,000 individuals. Statistical Analysis All statistical analyses were performed in R version 4.4.1 (R Core Team 2024 ). First, we modelled trends in the frequency of diurnal raptor and owl admissions over time using a series of Poisson Generalized Linear Mixed-effects Models (GLMMs) with “year” as a fixed effect and “centre_id” as a random intercept. Year was centred to improve model stability. Models were fitted using the lme4 R package (Bates et al. 2015 ), and the significance of “year” was assessed with Type II Wald χ² tests using the car R package (Fox and Weisberg 2019 ). Overdispersion was checked using Pearson residuals. Three separate Poisson models were run: 1) for all species combined (i.e., diurnal raptors and owls), 2) for diurnal raptors only, and 3) for owls only. Next, we assessed whether a particular species was admitted due to vehicle collisions more frequently than expected by chance given species level estimated UK breeding population sizes. For each species, we calculated the expected number of admissions proportional to its population size: Observed admissions were then compared to these expected values using a one-sided exact binomial test in base R. The null hypothesis assumed that each individual had an equal probability of being admitted due to a vehicle collision, proportional to the species’ breeding population size. Following this, we modelled the probability of raptor mortality after admission to WRCs due to collisions with road vehicles. We created a new binary response variable termed “died_bin” ( i.e. , died = 1, survived = 0) and used a GLMM with a binomial error distribution and logit link. The binary “died_bin” mortality variable was fitted as the response, with categorical variables: “road type” (major or minor), “season” (spring, summer, autumn or winter), “landscape type” (closed, open or urban), and “age” (adult or juvenile) fitted as fixed effects. To account for variation in mortality probabilities over time, we fitted admission “year” as a random intercept. Initial exploration indicated that “centre_id” contributed zero variance and led to a singular fit when included as a random effect. Therefore, we only retained “year” as a random effect. Predicted mortality probabilities and 95% confidence intervals were obtained using the allEffects() function within the effects R package (Fox 2009 ), providing marginal estimates for each predictor adjusted for the other covariates and the random effect of “year”. We assessed collinearity among predictor variables to ensure model stability. A simple binomial generalized linear model (GLM) was fitted using the binary “died_bin” mortality response variable, with “road type”, “landscape type”, “season”, and “age” as predictors. Variance inflation factors (VIFs) were calculated for each predictor to quantify multicollinearity using the car R package, with values exceeding 5–10 considered indicative of potential collinearity issues (Fox and Weisberg 2019 ). This approach allowed us to identify highly correlated variables prior to inclusion in mixed-effects models. Subsequently, VIF values showed no correlations between predictor variables therefore all were retained in our final model (Table S1 ). RESULTS Between 2001 and 2019, we compiled 786 raptor-vehicle collision admissions comprising 10 raptor species to four wildlife rehabilitation centres in southwest England and Wales. Of these, we geo-referenced 586 records (75%) which were included in the subsequent analysis (Fig. 1 ). By WRC, most admissions were provided by Gower Bird Hospital (n = 313, 53.4%), followed by Secret World Rescue (n = 141, 24.1%), Cuan Wildlife Rescue (n = 116, 19.8%), and Wild Wings Birds of Prey (n = 16, 2.7%) (Table S2). The most frequently admitted species was the Common Buzzard ( Buteo buteo ) (n = 224, 38.2% of all admissions), followed by the Tawny Owl ( Strix aluco ) (n = 223, 38%), Western Barn Owl ( Tyto alba ) (n = 58, 9.9%), Common Kestrel ( Falco tinnunculus ) (n = 29, 4.9%), and Eurasian Sparrowhawk ( Accipiter nisus ) (n = 26, 4.4%) (Table 1 ). By age class, most admissions were adult birds (n = 382, 65.2%), with juveniles representing a fifth of all admissions (n = 130, 22.2%), and 74 admissions (12.6%) where the age remained unknown. By road type, most collisions occurred on minor roads (n = 376, 64.2%), rather than major roads (n = 210, 35.8%). Overall, 340 birds (58%) died following collisions with road vehicles, while the remaining 246 (42%) survived; including a single bird that remained in captivity following admission (Table 1 ). Table 1 Total number of raptor-vehicle collision admissions to four wildlife rehabilitation centres in England and Wales between 2001–2019. Data are ordered by most frequently admitted species, with summed fates (died or survived) per species. Survived data include a single record where the bird was kept in captivity, all other records refer to the release of the birds back into the wild following treatment. Species Total number of admissions (%) Died (%) Survived (%) Common Buzzard ( Buteo buteo ) 224 (38.2) 140 (41.2) 84 (34.1) Tawny Owl ( Strix aluco ) 223 (38) 116 (34.1) 107 (43.5) Western Barn Owl ( Tyto alba ) 58 (9.9) 32 (9.4) 26 (10.6) Common Kestrel ( Falco tinnunculus ) 29 (4.9) 20 (5.9) 9 (3.7) Eurasian Sparrowhawk ( Accipiter nisus ) 26 (4.4) 15 (4.4) 11 (4.5) Little Owl ( Athene noctua ) 9 (1.5) 5 (1.5) 4 (1.6) Red Kite ( Milvus milvus ) 9 (1.5) 6 (1.8) 3 (1.2) Peregrine Falcon ( Falco peregrinus ) 6 (1) 4 (1.2) 2 (0.8) Short-eared Owl ( Asio flammeus ) 1 (0.2) 1 (0.3) 0 (0) Eurasian Hobby ( Falco subbuteo ) 1 (0.2) 1 (0.3) 0 (0) Total 586 (100) 340 (100) 246 (100) Trends in admissions over time Throughout the study period, despite a peak in admissions between 2014–2016, there was no significant overall change in annual admissions when considering all species together ( χ² = 2.527, df = 1, P = 0.119), suggesting that the total number of birds admitted per year remained relatively stable (Fig. 2 ; Table S3). This pattern was evident when considering diurnal raptors alone ( χ² = 0.254, df = 1, P = 0.614), however, there was a gradual decline in owl-vehicle collisions over the study period ( χ² = 11.462, df = 1, P < 0.001), corresponding to an approximate 3.7% decrease in admissions per year (Fig. 2 ; Table S3). Species-specific vehicle collision admissions When assessing whether raptor species were admitted due to vehicle collisions more frequently than expected based on their estimated UK breeding populations, we found that some species were overrepresented among admissions relative to expectation, whereas others were underrepresented (Table 2 ). For example, Tawny Owl and Western Barn Owl showed significantly more admissions than expected by chance (Binomial test: P < 0.001 for both), whereas larger raptors such as Common Buzzard were slightly overrepresented within our collision dataset ( P = 0.004) (Table 2 ). Conversely, several species including Eurasian Sparrowhawk, Peregrine Falcon, and Red Kite did not differ significantly from expected admission rates ( i.e. , P > 0.1) (Table 2 ). Table 2 Observed and expected raptor-vehicle admissions to four wildlife rehabilitation centres in England and Wales between 2001–2019. Expected number of vehicle collision admissions was calculated based on the number of admissions expected if all species experienced collisions proportional to their population size, and the P value is in relation to a one-sided binomial test assessing whether each species was admitted due to vehicle collisions more often than expected by chance. Bold = species with significant number of raptor-vehicle collisions than expected by chance. *Estimates derived from the British Trust for Ornithology’s BirdFacts database. Species Estimated number of breeding individuals in the UK* Total number of vehicle collision admissions Expected number of vehicle collision admissions P value Tawny Owl ( Strix aluco ) 100,000 223 153 < 0.001 Western Barn Owl ( Tyto alba ) 8,000 58 12 < 0.001 Common Buzzard ( Buteo buteo ) 126,000 224 193 0.004 Peregrine Falcon ( Falco peregrinus ) 3,500 6 5 0.447 Little Owl ( Athene noctua ) 7,200 9 11 0.772 Short-eared Owl ( Asio flammeus ) 1,240 1 2 0.851 Red Kite ( Milvus milvus ) 8,800 9 13 0.922 Eurasian Hobby ( Falco subbuteo ) 4,100 1 6 0.998 Eurasian Sparrowhawk ( Accipiter nisus ) 62,000 26 95 1.000 Common Kestrel ( Falco tinnunculus ) 62,000 29 95 1.000 Mortality risk associated with road vehicle collisions Road type was a strong predictor of mortality in raptors that had been hit by road vehicles ( χ² = 24.058, df = 1, P < 0.001; Fig. 3 a; Table S4). Predicted probabilities of death derived from the model indicated that raptors admitted from major roads had a probability of mortality of ~ 72%, compared with ~ 49% on minor roads (Fig. 3 a). In terms of odds, raptors struck on major roads had ~ 2.7 times higher odds of death than those struck on minor roads, equivalent to a ~ 168% increase in the odds of mortality. Contrastingly, there were no effects of season ( χ² = 1.792, df = 3, P = 0.617) (Fig. 3 b), landscape type ( χ² = 3.819, df = 2, P = 0.148) (Fig. 3 c), nor age class ( χ² = 0.777, df = 1, P = 0.378) (Fig. 3 d) on raptor mortality due to collisions with road vehicles. DISCUSSION Using nearly two decades of admissions data from wildlife rehabilitation centres, we provide a regional assessment of raptor collisions with road vehicles across heterogeneous rural to peri-urban landscapes in southwest England and Wales. Our findings reveal stable collision admissions over time, contrasting trends between diurnal raptors and owls, strong species-level differences in collision representation relative to population size, and a pronounced increase in mortality risk associated with major roads. Together, our results highlight how road infrastructure and species-specific ecology interact to shape collision risk and post-collision outcomes in urban landscapes, confirming findings from a recent systematic review which found that human infrastructure poses a substantial threat to raptors globally (Panter et al. 2026 ). Temporal patterns in raptor and owl vehicle collisions Despite substantial increases in road traffic volume and continued expansion of transport infrastructure across the UK during the study period (Department for Transport 2021), we found no significant temporal trend in overall raptor admissions due to vehicle collisions. The absence of a clear increase in collision admissions over time suggests that factors beyond traffic volume alone may influence observed collision rates. For example, species’ population dynamics, temporal shifts in habitat use, and changes in reporting or detection probability may contribute to stable admission numbers despite growing traffic. Similar patterns of persistence in wildlife-vehicle collision rates have been observed in other contexts. For instance, national analyses in North America found that overall wildlife-vehicle collisions did not decline in proportion to large reductions in traffic volume during the COVID-19 pandemic, indicating that collision dynamics can be decoupled from traffic trends (Abraham and Mumma 2021 ). In contrast, when analysing only owl admissions we detected a decline in incidents over the study period, driven primarily by Tawny Owl and Western Barn Owl records. This pattern may reflect documented population declines in some owl species linked to agricultural intensification, loss of rough grassland and prey availability, and secondary poisoning (Leech et al. 2005 ; Walker et al. 2008 ). Reduced population size would be expected to lead to fewer collision events, even if per capita collision risk remains unchanged. Alternatively, changes in road verge management or lighting regimes may disproportionately affect nocturnal species by altering foraging behaviour near roads, as anthropogenic noise and artificial light at night can reduce foraging efficiency and alter movement patterns in nocturnal animals (Senzaki et al. 2016 ; Sordello et al. 2025 ). Disentangling these mechanisms requires integrated population monitoring alongside collision datasets, but our findings suggest that temporal trends in collision admissions should not be interpreted independently of broader population dynamics. Species-specific representation in collision admissions We found clear species-level differences in representation within the collision dataset relative to estimated UK breeding population sizes. Owls, particularly Tawny Owl and Western Barn Owl, were strongly overrepresented, whereas species such as Peregrine Falcon, Red Kite, and Eurasian Sparrowhawk occurred at or below expected frequencies within our admissions dataset. Previous research has suggested that Tawny Owls may often become victims to collisions with road vehicles due to blinding at night and their propensity to hunt and perch along roadway vegetation at night (Panter et al. 2022 ). Western Barn Owls often fly low over field margin habitats and hedgerows while hunting at dusk (McHugh et al. 2024 ), which may increase their risk of collision with road vehicles in agricultural landscapes (Hanmer and Robinson 2021 ). Additionally, nocturnal activity coincides with reduced driver visibility, further increasing collision risk. On the other hand, species such as Peregrine Falcon and Eurasian Sparrowhawk are aerial hunters and rarely interact with roadways when feeding, despite both species readily adapting to urban landscapes (Newton 1979 ). The slight overrepresentation of Common Buzzards is notable given their large and expanding UK population (Stevens et al. 2019 ; Arraut et al. 2021 ). This species frequently scavenges carrion, including roadkill, and may be struck while feeding on carcasses or while taking off from roadside verges (Cieśluk et al. 2024 ). This behaviour may elevate collision risk disproportionately relative to population size, particularly on rural roads where carcass removal is infrequent. Road type as a determinant of mortality risk Road type was the strongest predictor of raptor mortality following vehicle collisions, with birds struck on major roads experiencing substantially higher probabilities of death than those struck on minor roads. This pattern likely reflects higher vehicle speeds, increased traffic volume, and reduced reaction time for both drivers and birds on major roads (DeVault et al. 2014 ). A study by Gagné et al. ( 2015 ) reported that collision risk rises with both road speed and narrowness. We observed more collisions on minor (and assumed narrower) roads, indicating that even lower-speed roads can pose high risks where road width constrains bird movement, emphasising the need to consider road geometry alongside traffic speed within mitigation planning. Although collisions occurred more frequently on minor roads, impacts on major roads were far more lethal, emphasising an important distinction between collision frequency and collision severity. Conservation practitioners should account for this distinction when prioritising locations and road types for the implementation of mitigation and collision-reduction measures. This finding aligns with studies showing that high-speed roads function as population sinks for many vertebrates, even when overall collision numbers are lower (Balčiauskas et al. 2025 ). From an urban ecosystems perspective, this highlights the outsized ecological impact of major transportation corridors, particularly as they intersect peri-urban and rural landscapes that support high raptor activity. Targeted mitigation measures such as verge vegetation management, carcass removal, and road-specific warning signage may therefore yield disproportionate conservation benefits if focused on major roads (Bullock et al. 2024 ). No influence of season, landscape context, and age on raptor-vehicle collision mortality Contrary to expectations, we detected no effect of season, surrounding landscape type, or age class on post-collision mortality. Seasonal effects on collision frequency have been documented in some raptors, e.g. , Tawny Owl, often linked to juvenile dispersal or breeding activity (Raymond et al. 2021 ), but our results suggest that once a collision occurs, the probability of survival is largely independent of timing within the year. This may indicate that impact severity and immediate injury overwhelm seasonal or physiological differences among individuals. Similarly, the absence of a landscape effect suggests that local road characteristics and traffic conditions may be more important determinants of survival than the broader land cover context within which collisions occur. While urban, open, and closed landscapes differ in traffic density and road design, our mortality analysis indicates that these differences do not translate into consistent variation in post-admission outcomes. Age-related differences in collision susceptibility have been reported elsewhere (Orozco-Valor et al. 2024 ), but our findings imply that adults and juveniles are similarly vulnerable once struck, potentially due to comparable body size and trauma exposure. Limitations and implications for urban ecology and conservation As with all rehabilitation centre datasets, our study is subject to reporting and detection biases. It is assumed that most of our admission records are of injured birds that were found but includes some individuals admitted as dead on arrival. Unfortunately, the resolution of our admissions data does not allow for this differentiation to be made, and it is therefore assumed that mortality at the scene is likely underestimated, particularly on major roads where carcasses may be rapidly removed. Additionally, population estimates used to assess species-level representation were limited to breeding individuals and did not account for non-breeders or seasonal influxes, which may influence collision exposure. Despite these limitations, our study provides valuable insights into how road infrastructure shapes raptor mortality across human-modified landscapes. By integrating spatial, demographic, and infrastructural variables, we demonstrate that road type, rather than landscape context or seasonality, is the dominant predictor of post-collision survival. As road networks continue to expand globally, particularly at the urban-rural interface, understanding and mitigating their impacts on apex and mesopredators will be increasingly important for maintaining functional urban ecosystems. Future research should integrate raptor collision records with fine-scale traffic data (Heeres et al. 2025 ), telemetry-derived movement information (Panter et al. 2025 ), and enhanced reporting systems, such as online citizen-science portals (Shilling et al. 2020 ), to improve estimates of per capita collision risk and assess mitigation effectiveness. Such multidimensional approaches will be essential for balancing transport development with the conservation of raptor populations in increasingly urbanised and fragmented landscapes. Declarations FUNDING The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. COMPETING INTERESTS The authors have no relevant financial or non-financial interests to disclose. AUTHOR CONTRIBUTIONS All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Richard Laurie and Connor Panter. The first draft of the manuscript was written by Richard Laurie and all authors commented on previous versions of the manuscript. Rachel White and Connor Panter supervised the project. All authors read and approved the final manuscript. ACKNOWLEDGEMENTS The authors would like to thank the following from various wildlife rehabilitation centres for providing admissions data: Simon Allen (Gower Bird Hospital), Elizabeth Mullineaux (Secret World Wildlife Rescue), Nikki Backhouse (Cuan Wildlife Rescue) and Carole-Ann Rose (Wild Wings Birds of Prey). References Abraham JO, Mumma MA (2021) Elevated wildlife-vehicle collision rates during the COVID-19 pandemic. 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Biol Conserv 316:111746. https://doi.org/10.1016/j.biocon.2026.111746 Panter CT, Nebel C, Raab M, Strauss V, Freytag C, Wojta M, Böing H, Hacker P, Raab R, Windt J et al (2025) A LEAP forward in wildlife conservation: a standardized framework to determine mortality causes in large GPS-tagged birds. Ecol Evol 15: e70975. https://doi.org/10.1002/ece3.70975 Panter CT, Allen S, Backhouse N, Mullineaux E, Rose C-A, Amar A (2022) Causes, temporal trends, and the effects of urbanization on admissions of wild raptors to rehabilitation centers in England and Wales. Ecol Evol 12:e8856. https://doi.org/10.1002/ece3.8856 Pynn TP, Pynn BR (2004) Moose and other large animal wildlife vehicle collisions: implications for prevention and emergency care . J Emerg Nursing 30:542–547. https://doi.org/10.1016/j.jen.2004.07.084 QGIS Development Team (2025) QGIS Geographic Information System, version 3.38. Open Source Geospatial Foundation Project. Available at: https://qgis.org R Core Team (2024) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ Raymond S, Schwartz ALW, Thomas RJ, Chadwick E, Perkins SE (2021) Temporal patterns of wildlife roadkill in the UK. PLoS ONE 19:e0310789. https://doi.org/10.1371/journal.pone.0258083 Rodríguez-Estrella R, Donázar JA, Hiraldo F (1998) Raptors as indicators of environmental change in the scrub habitat of Baja California Sur, Mexico. Conserv Biol 12:921–925. https://doi.org/10.1111/j.1523-1739.1998.97044.x Rowland C, Morton D, Carrasco Torneo L, McShane G, O’Neil A, Wood C (2017) Land Cover Map 2015 (25m raster, GB). NERC Environmental Information Data Centre. https://doi.org/10.5285/bb15e200-9349-403c-bda9-b430093807c7 Santos SM, Mira A, Salgueiro PA, Costa P, Medinas D, Beja P (2016) Avina trait-mediated vulnerability to road traffic collisions. Biol Conserv 200:122–130. https://doi.org/10.1016/j.biocon.2016.06.004 Schwartz ALW, Williams HF, Chadwick E, Thomas RJ, Perkins S (2018) Roadkill scavenging behaviour in an urban environment. J Urban Ecol 4:juy006. https://doi.org/10.1093/jue/juy006 Senzaki M, Yamaura Y, Francis CD, Nakamura F (2016) Traffic noise reduces foraging efficiency in wild owls. Sci Rep 6:30602. https://doi.org/10.1038/srep30602 Shilling F, Collinson W, Bil M, Vercayie D, Heigl F, Perkins SE, MacDougall S (2020) Designing wildlife-vehicle conflict observation systems to inform ecology and transportation studies. Biol Conserv 251:108797. https://doi.org/10.1016/j.biocon.2020.108797 Slater SJ, Maloney DM, Taylor JM (2022) Golden eagle use of winter roadkill and response to vehicles in the western United States. J Wildl Manag 86:e22246. https://doi.org/10.1002/jwmg.22246 Smith JA, Dwyer JF (2016) Avian interactions with renewable energy infrastructure: an update. The Condor 118:411–423. https://doi.org/10.1650/CONDOR-15-61.1 Sordello R, Coulon A, Reyjol Y (2025) A narrative review of the impact of anthropogenic light and noise on owls. Ibis 168:5–24. https://doi.org/10.1111/ibi.13435 Stevens M, Murn C, Hennessy R (2019) Population change of common buzzards Buteo buteo in central and southern England between 2011 and 2016. Bird Study 66:378–389. https://doi.org/10.1080/00063657.2019.1693960 Thompson LJ, Hoffman B, Brown M (2013) Causes of admissions to a raptor rehabilitation centre in KwaZulu-Natal, South Africa. African Zool 48:359–366. https://doi.org/10.1080/15627020.2013.11407603 Walker LA, Turk A, Long SM, Wienburg CL, Best J, Shore RF (2008) Second generation anticoagulant rodenticides in tawny owls ( Strix aluco ) from Great Britain. Sci Tot Environ 392:93–98. https://doi.org/10.1016/j.scitotenv.2007.10.061 Wendell MD, Sleeman JM, Kratz G (2002) Retrospective study of morbidity and mortality of raptors admitted to Colorado state university veterinary teaching hospital during 1995 to 1998. J Wild Disease 38:101–106. https://doi.org/10.7589/0090-3558-38.1.101 Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 08 May, 2026 Reviews received at journal 10 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers invited by journal 09 Mar, 2026 Editor assigned by journal 03 Mar, 2026 Submission checks completed at journal 03 Mar, 2026 First submitted to journal 28 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8997953","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604578932,"identity":"4408784b-a757-450b-81c9-2e340c14574b","order_by":0,"name":"Richard Laurie","email":"","orcid":"","institution":"University of Brighton","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Laurie","suffix":""},{"id":604578935,"identity":"cf6f777d-99b5-41f8-9a0d-ebd0edfba85c","order_by":1,"name":"Rachel L. White","email":"","orcid":"","institution":"University of Brighton","correspondingAuthor":false,"prefix":"","firstName":"Rachel","middleName":"L.","lastName":"White","suffix":""},{"id":604578936,"identity":"f2cbcd1c-67e8-4c58-8671-67f9728dc0bc","order_by":2,"name":"Connor T. Panter","email":"data:image/png;base64,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","orcid":"","institution":"University of Nottingham","correspondingAuthor":true,"prefix":"","firstName":"Connor","middleName":"T.","lastName":"Panter","suffix":""}],"badges":[],"createdAt":"2026-02-28 21:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8997953/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8997953/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104481613,"identity":"66f27fbf-cac1-4bdc-841b-930202b60306","added_by":"auto","created_at":"2026-03-12 09:28:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":604385,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of 586 raptor-vehicle collisions where the birds were admitted to four wildlife rehabilitation centres in England and Wales between 2001–2019.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8997953/v1/ca6b86b7de623ab3cc0546c7.png"},{"id":104481614,"identity":"36795a7e-951b-4835-9beb-b842a34b5eb9","added_by":"auto","created_at":"2026-03-12 09:28:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132086,"visible":true,"origin":"","legend":"\u003cp\u003eTrends over time in the number of raptor-vehicle collision admissions to four wildlife rehabilitation centres in England and Wales between 2001–2019. Data represents 10 raptor species and 586 admission records presented by pooled taxonomic groups (owls and diurnal raptors).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8997953/v1/177a125e4a5c64d01fccc906.png"},{"id":104481615,"identity":"7a96ac1a-dc88-4fd9-bd96-550e7d24a0fb","added_by":"auto","created_at":"2026-03-12 09:28:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":242900,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted probabilities of raptor mortality following 586 vehicle collisions across England and Wales between 2001–2019. Points show model-predicted probabilities with error bars indicating 95% confidence intervals. Panels show effects of (a) road type, (b) season, (c) landscape type, and (d) age class on the modelled probability of mortality following admission to four wildlife rehabilitation centres.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8997953/v1/35580c84ec06ed9729647204.png"},{"id":104780697,"identity":"a64ef9e8-27ef-40a5-937d-8ee77ff57441","added_by":"auto","created_at":"2026-03-17 07:53:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1819288,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8997953/v1/29228963-b5e9-468d-a7f9-bb216079b760.pdf"},{"id":104481616,"identity":"c684dc11-9996-46c6-a334-4427b9241a13","added_by":"auto","created_at":"2026-03-12 09:28:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26312,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8997953/v1/10b8629ca734f5092159b755.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Major roads drive higher raptor mortality: insights from long-term rehabilitation data","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGlobal urbanisation and human population growth have accelerated the development of transportation infrastructure, resulting in the proliferation of roads and vehicles across landscapes (Ibisch et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Currently the global road network spans over 21.6\u0026nbsp;million kilometres and is projected to grow by 14\u0026ndash;23% by 2050, making road expansion one of the most urgent challenges for conservation biologists (Meijer et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the UK alone, roads cover\u0026thinsp;~\u0026thinsp;398,900 km, and by 2021, 40.3\u0026nbsp;million people were registered as private car owners (Department of Transport 2021a, 2021b), highlighting the scale, density and dependence on road infrastructure and its potential impact on wildlife across nearly all terrestrial habitats.\u003c/p\u003e \u003cp\u003eRoad expansion contributes to habitat loss, fragmentation and edge effects, pollution, and alters wildlife movement patterns and behaviour (Forman and Alexander \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Coffin \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Cooke et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). One of the most pronounced direct effects includes wildlife-vehicle collisions, which are recognised as a major driver of vertebrate mortality worldwide (Gunson et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pagany \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Unlike other forms of anthropogenic mortality, vehicle collisions have an indiscriminate impact on wildlife regardless of demography, physical condition, or conservation status (Hill et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Yet, studies suggest that actual wildlife-vehicle collision mortality rates may be significantly underreported due to carcass removal via scavengers and detection bias (Hill et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBirds tend to be underrepresented in wildlife-vehicle collisions research, with research efforts concentrating on large mammals due to risks towards human safety (Pynn and Pynn \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Comparatively little is known regarding the effects of road vehicles on birds, which often interact with human infrastructure including roads (Bullock et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Panter et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Avian vulnerability to vehicle collisions is influenced by a range of interacting factors, including species-specific flight behaviour, body size, sensory limitations, traffic volume and speed, and the spatial configuration of roads relative to habitats and food resources (Santos et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Species that forage or hunt along road verges, scavenge carrion, or regularly cross roads at low flight heights are particularly susceptible (Husby \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Within this broader context, raptors are disproportionately affected by road\u0026ndash;vehicle collisions due to their attraction to roadside prey and carrion, low-altitude hunting strategies, and delayed take-off responses (Hager \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bullock et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRaptors, \u003cem\u003ei.e.\u003c/em\u003e, Accipitriformes, Cathartiformes, Falconiformes, Strigiformes and Cariamiformes (McClure et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), are valuable indicators of ecosystem health (Rodr\u0026iacute;guez-Estrella et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Don\u0026aacute;zar et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As apex predators, raptors influence multiple trophic levels, and population declines can initiate trophic cascades with long-lasting effects transcending entire food webs (O\u0026rsquo;Bryan et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Threats to raptors include direct and indirect persecution (Buij et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), climate change (Mart\u0026iacute;nez-Ruiz et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), collisions with energy infrastructure (Smith and Dwyer \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kolnegari et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and other elements associated with urban landscapes such as collisions with road vehicles (Bullock et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A recent global systematic review of causes of raptor morbidity and mortality highlighted the impact of the urban environment and human infrastructure on raptors (see Panter et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Consequently, vehicle collisions are recognised as one of the leading causes of raptor mortality globally (Hager \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dwyer et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Boal and Dykstra \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRaptor species\u0026rsquo; ecology may explain propensity to be hit by road vehicles. Scavenging raptors may be particularly susceptible to vehicle collisions due to their attraction to roadside carrion (Cieśluk et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Scavengers feed opportunistically, and the presence of anthropogenic food sources, including roadkill, can significantly influence their behaviour and increase time spent near roads (Schwartz et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, previous research found that owls may be disproportionately susceptible to road\u0026ndash;vehicle collisions (Panter et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Owl-vehicle collisions may be exacerbated by their low foraging flight heights, blinding by vehicles at night, and difficulty detecting prey in noisy environments (De Jong et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRoad-specific features, including maximum speed limits and proximity to prey-rich habitats such as grasslands, may further influence collision probabilities (Hanmer and Robinson \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, agricultural intensification and habitat degradation are displacing both raptors and their prey toward roadside environments, increasing exposure and collision risks (Butet et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The potential threat of raptor-vehicle collisions is particularly pronounced in developed countries, such as the UK, where over 70% of the natural landscape lies within 700 metres of a road (Cooke et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Understanding the environmental and anthropogenic predictors of these collisions is essential for developing targeted national conservation and mitigation strategies.\u003c/p\u003e \u003cp\u003eGiven ongoing gaps in understanding the drivers of raptor-vehicle collisions, there is a need for research that links species, road characteristics, seasonality, and surrounding habitat types at the landscape level to guide targeted mitigation strategies across the UK. Here, we use admissions data from wildlife rehabilitation centres (WRCs) in southwest England and Wales to investigate patterns of raptor-vehicle collisions over a 19-year period (2001\u0026ndash;2019). Specifically, we examine how 1) species, 2) road type (minor vs. major), 3) season, 4) surrounding landscape type, and 5) age class influence raptor mortality outcomes following collisions. This work builds on Panter et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) by leveraging WRC admissions data to examine how taxonomic, temporal and environmental characteristics influence raptor-vehicle collisions and their associated mortality.\u003c/p\u003e \u003cp\u003eWRC admission records provide a valuable source of information on the primary causes of morbidity and mortality in wild raptor populations and have been widely used to assess anthropogenic threats to birds of prey (Morishita et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Wendell et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Molina-L\u0026oacute;pez et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Thompson et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Panter et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Building on previous findings, we hypothesised that scavenging species, such as Common Buzzard (\u003cem\u003eButeo buteo\u003c/em\u003e), would be overrepresented in collision admissions due to the consumption of carrion along roadways (Schwartz et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Slater et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We further expected mortality probability following collisions to vary with road type, landscape type, season, and age class. Collisions on major roads were predicted to result in higher mortality due to greater vehicle speeds and traffic volumes, increasing impact severity and reducing opportunities for avoidance (DeVault et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bullock et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We also anticipated higher mortality risk in open landscapes (Hanmer and Robinson \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), where raptors foraging or commuting at low altitude may be more exposed to traffic and have fewer visual or structural cues to facilitate avoidance compared with more structurally complex habitats such as woodland or hedgerow networks. We also expect to find seasonal variation in mortality (Conard and Gipson \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), with higher mortality during winter and autumn when reduced daylight, adverse weather conditions, and lower prey availability may increase foraging along roads and reduce flight performance or reaction times. Finally, we predicted that juvenile birds would exhibit higher mortality than adults following collisions, reflecting lower flight experience, reduced hazard perception, and potentially poorer escape responses during high-risk encounters with vehicles (Orozco-Valor et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy region\u003c/h2\u003e\n \u003cp\u003eAdmissions data were sourced from four WRCs, namely: Cuan Wildlife Rescue in Shropshire, England (lat, long: 52.590, \u0026minus;\u0026thinsp;2.573), Gower Bird Hospital in Glamorgan, Wales (51.580, \u0026minus;\u0026thinsp;4.099), Secret World Wildlife Rescue in Somerset, England (51.206, \u0026minus;\u0026thinsp;2.964), and Wild Wings Birds of Prey in Cheshire, England (53.444, \u0026minus;\u0026thinsp;2.522) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) (Panter et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The study region spans a heterogeneous landscape characterised by a mosaic of rural, agricultural and peri-urban environments. Much of the region is dominated by low-intensity farmland, pastoral landscapes and upland areas with relatively low human population densities, interspersed with urban centres and transport infrastructure associated with towns and cities such as Bristol, Cardiff, Swansea, Shrewsbury and Chester. Road networks range from minor rural roads to major A-roads and motorways, providing broad representation of traffic environments across gradients of urbanisation and human activity.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAdmissions data\u003c/h3\u003e\n\u003cp\u003eAdmissions data were sourced from Panter et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), which presents case-by-case records of raptors admitted to WRCs in southwest/west England and Wales between 2001\u0026ndash;2019. For each record, the admitted bird was identified to species level, and the following information was extracted where available: 1) age (adult, juvenile, or unknown), 2) admission date (dd/mm/yyyy), 3) fate (released, captivity, or died), and, where possible, 4) location of the collision (latitude and longitude). Because only a single bird remained in captivity post-admission, this case was pooled with birds that were released and reclassified under a new factor level: \u0026ldquo;survived.\u0026rdquo; Raptor ages were not precise and were determined at the point of admission by WRC staff and veterinarians. To account for uncertainty in identifying fledging, juvenile, and immature individuals, ages were standardised into broad categories for analysis (\u003cem\u003ei.e.\u003c/em\u003e, \u0026ldquo;adult,\u0026rdquo; \u0026ldquo;juvenile,\u0026rdquo; and \u0026ldquo;unknown\u0026rdquo;).\u003c/p\u003e\n\u003cp\u003eWhere precise coordinates were unavailable, we georeferenced each admission record using descriptive qualitative data available alongside the individual record which was often provided by members of the public that discovered the bird, \u003cem\u003ee.g.\u003c/em\u003e, \u0026ldquo;the bird was found on the road in front of St Andrews church\u0026rdquo;. In cases where information provided was too limited to accurately geo-reference the admission record, we omitted those records from the dataset. Using the admission dates, we created a new categorical variable, \u0026ldquo;season,\u0026rdquo; and classified each record into four groups: \u0026ldquo;spring\u0026rdquo; (March\u0026ndash;May), \u0026ldquo;summer\u0026rdquo; (June\u0026ndash;August), \u0026ldquo;autumn\u0026rdquo; (September\u0026ndash;November), and \u0026ldquo;winter\u0026rdquo; (December\u0026ndash;February).\u003c/p\u003e\n\u003ch3\u003eRoad data\u003c/h3\u003e\n\u003cp\u003eGeoreferenced raptor-vehicle collision sites were manually cross-checked against Ordnance Survey (OS) mapping layers via EDINA Digimap (EDINA Digimap \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Original road classifications comprised \u0026ldquo;Motorway,\u0026rdquo; \u0026ldquo;Primary,\u0026rdquo; \u0026ldquo;A roads,\u0026rdquo; \u0026ldquo;B roads,\u0026rdquo; \u0026ldquo;Local,\u0026rdquo; and \u0026ldquo;Minor\u0026rdquo; roads. For analytical purposes, we regrouped these into two broader categories: 1) \u0026ldquo;major roads\u0026rdquo; (which included motorways/freeways, primary roads, and A roads) and 2) \u0026ldquo;minor roads\u0026rdquo; (including B roads, local roads, and minor roads). In the UK, A roads are major through-routes connecting towns and cities, while B roads are smaller, secondary routes connecting local areas and communities.\u003c/p\u003e\n\u003ch3\u003eLandscape type data\u003c/h3\u003e\n\u003cp\u003eWe downloaded land cover data at 25-metre resolution from the UKCEH\u0026rsquo;s Land Cover Map 2015 dataset (Rowland et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). The 2015 land cover dataset was used to coincide with approximate temporal coverage of the admissions dataset. We computed circular buffers (500 metre radii) around each georeferenced record, to account for potential uncertainty in exact collision locations, and extracted the proportionally dominant land cover type within each buffer using the zonal statistics feature within QGIS version 3.38 (QGIS Development Team \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). From there, we reclassified the 21 local-scale habitat types into three broad landscape categories: 1) \u0026ldquo;open\u0026rdquo; (\u003cem\u003ei.e.\u003c/em\u003e, estuaries, moors and heathland, natural grasslands, non-irrigated arable land, pastures, and water bodies), 2) \u0026ldquo;closed\u0026rdquo; (\u003cem\u003ei.e.\u003c/em\u003e, coniferous forest, broad-leaved forest, transitional woodland-shrub, and mixed forest), and 3) \u0026ldquo;urban\u0026rdquo; (\u003cem\u003ei.e.\u003c/em\u003e, airports, construction sites, continuous and discontinuous urban fabrics, green urban areas, industrial/commercial, mineral extraction sites, ports, road/rail networks, and sport and leisure facilities).\u003c/p\u003e\n\u003ch3\u003eEstimating proportional representativeness within the dataset\u003c/h3\u003e\n\u003cp\u003eTo quantify whether a particular species was over- or underrepresented in our dataset, we explored the relationship between relative population size for each species throughout the UK and the relative proportion of vehicle collision records from the four WRCs. Species population estimates were manually extracted from the British Trust for Ornithology\u0026rsquo;s BirdFacts database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bto.org/learn/about-birds/birdfacts\u003c/span\u003e\u003c/span\u003e). We extracted the population estimates as number of breeding pairs and multiplied this by two to estimate number of breeding individuals (this estimate does not include non-breeding individuals). Where ranged population estimates were provided, \u003cem\u003ee.g.\u003c/em\u003e, 1,500\u0026ndash;2,500 breeding pairs, we used the higher estimate, \u003cem\u003ei.e.\u003c/em\u003e, 5,000 individuals.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses were performed in R version 4.4.1 (R Core Team \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). First, we modelled trends in the frequency of diurnal raptor and owl admissions over time using a series of Poisson Generalized Linear Mixed-effects Models (GLMMs) with \u0026ldquo;year\u0026rdquo; as a fixed effect and \u0026ldquo;centre_id\u0026rdquo; as a random intercept. Year was centred to improve model stability. Models were fitted using the \u003cem\u003elme4\u003c/em\u003e R package (Bates et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), and the significance of \u0026ldquo;year\u0026rdquo; was assessed with Type II Wald \u0026chi;\u0026sup2; tests using the \u003cem\u003ecar\u003c/em\u003e R package (Fox and Weisberg \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Overdispersion was checked using Pearson residuals. Three separate Poisson models were run: 1) for all species combined (i.e., diurnal raptors and owls), 2) for diurnal raptors only, and 3) for owls only.\u003c/p\u003e\n \u003cp\u003eNext, we assessed whether a particular species was admitted due to vehicle collisions more frequently than expected by chance given species level estimated UK breeding population sizes. For each species, we calculated the expected number of admissions proportional to its population size:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003eObserved admissions were then compared to these expected values using a one-sided exact binomial test in base R. The null hypothesis assumed that each individual had an equal probability of being admitted due to a vehicle collision, proportional to the species\u0026rsquo; breeding population size.\u003c/p\u003e\n \u003cp\u003eFollowing this, we modelled the probability of raptor mortality after admission to WRCs due to collisions with road vehicles. We created a new binary response variable termed \u0026ldquo;died_bin\u0026rdquo; (\u003cem\u003ei.e.\u003c/em\u003e, died\u0026thinsp;=\u0026thinsp;1, survived\u0026thinsp;=\u0026thinsp;0) and used a GLMM with a binomial error distribution and logit link. The binary \u0026ldquo;died_bin\u0026rdquo; mortality variable was fitted as the response, with categorical variables: \u0026ldquo;road type\u0026rdquo; (major or minor), \u0026ldquo;season\u0026rdquo; (spring, summer, autumn or winter), \u0026ldquo;landscape type\u0026rdquo; (closed, open or urban), and \u0026ldquo;age\u0026rdquo; (adult or juvenile) fitted as fixed effects. To account for variation in mortality probabilities over time, we fitted admission \u0026ldquo;year\u0026rdquo; as a random intercept. Initial exploration indicated that \u0026ldquo;centre_id\u0026rdquo; contributed zero variance and led to a singular fit when included as a random effect. Therefore, we only retained \u0026ldquo;year\u0026rdquo; as a random effect. Predicted mortality probabilities and 95% confidence intervals were obtained using the allEffects() function within the \u003cem\u003eeffects\u003c/em\u003e R package (Fox \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e), providing marginal estimates for each predictor adjusted for the other covariates and the random effect of \u0026ldquo;year\u0026rdquo;.\u003c/p\u003e\n \u003cp\u003eWe assessed collinearity among predictor variables to ensure model stability. A simple binomial generalized linear model (GLM) was fitted using the binary \u0026ldquo;died_bin\u0026rdquo; mortality response variable, with \u0026ldquo;road type\u0026rdquo;, \u0026ldquo;landscape type\u0026rdquo;, \u0026ldquo;season\u0026rdquo;, and \u0026ldquo;age\u0026rdquo; as predictors. Variance inflation factors (VIFs) were calculated for each predictor to quantify multicollinearity using the \u003cem\u003ecar\u003c/em\u003e R package, with values exceeding 5\u0026ndash;10 considered indicative of potential collinearity issues (Fox and Weisberg \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). This approach allowed us to identify highly correlated variables prior to inclusion in mixed-effects models. Subsequently, VIF values showed no correlations between predictor variables therefore all were retained in our final model (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eBetween 2001 and 2019, we compiled 786 raptor-vehicle collision admissions comprising 10 raptor species to four wildlife rehabilitation centres in southwest England and Wales. Of these, we geo-referenced 586 records (75%) which were included in the subsequent analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy WRC, most admissions were provided by Gower Bird Hospital (n\u0026thinsp;=\u0026thinsp;313, 53.4%), followed by Secret World Rescue (n\u0026thinsp;=\u0026thinsp;141, 24.1%), Cuan Wildlife Rescue (n\u0026thinsp;=\u0026thinsp;116, 19.8%), and Wild Wings Birds of Prey (n\u0026thinsp;=\u0026thinsp;16, 2.7%) (Table S2). The most frequently admitted species was the Common Buzzard (\u003cem\u003eButeo buteo\u003c/em\u003e) (n\u0026thinsp;=\u0026thinsp;224, 38.2% of all admissions), followed by the Tawny Owl (\u003cem\u003eStrix aluco\u003c/em\u003e) (n\u0026thinsp;=\u0026thinsp;223, 38%), Western Barn Owl (\u003cem\u003eTyto alba\u003c/em\u003e) (n\u0026thinsp;=\u0026thinsp;58, 9.9%), Common Kestrel (\u003cem\u003eFalco tinnunculus\u003c/em\u003e) (n\u0026thinsp;=\u0026thinsp;29, 4.9%), and Eurasian Sparrowhawk (\u003cem\u003eAccipiter nisus\u003c/em\u003e) (n\u0026thinsp;=\u0026thinsp;26, 4.4%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy age class, most admissions were adult birds (n\u0026thinsp;=\u0026thinsp;382, 65.2%), with juveniles representing a fifth of all admissions (n\u0026thinsp;=\u0026thinsp;130, 22.2%), and 74 admissions (12.6%) where the age remained unknown. By road type, most collisions occurred on minor roads (n\u0026thinsp;=\u0026thinsp;376, 64.2%), rather than major roads (n\u0026thinsp;=\u0026thinsp;210, 35.8%). Overall, 340 birds (58%) died following collisions with road vehicles, while the remaining 246 (42%) survived; including a single bird that remained in captivity following admission (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal number of raptor-vehicle collision admissions to four wildlife rehabilitation centres in England and Wales between 2001\u0026ndash;2019. Data are ordered by most frequently admitted species, with summed fates (died or survived) per species. Survived data include a single record where the bird was kept in captivity, all other records refer to the release of the birds back into the wild following treatment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal number of admissions (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDied (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvived (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon Buzzard (\u003cem\u003eButeo buteo\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (34.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTawny Owl (\u003cem\u003eStrix aluco\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e223 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (43.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Barn Owl (\u003cem\u003eTyto alba\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (10.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon Kestrel (\u003cem\u003eFalco tinnunculus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurasian Sparrowhawk (\u003cem\u003eAccipiter nisus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (4.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLittle Owl (\u003cem\u003eAthene noctua\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed Kite (\u003cem\u003eMilvus milvus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeregrine Falcon (\u003cem\u003eFalco peregrinus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort-eared Owl (\u003cem\u003eAsio flammeus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurasian Hobby (\u003cem\u003eFalco subbuteo\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e586 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e340 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eTrends in admissions over time\u003c/h3\u003e\n\u003cp\u003eThroughout the study period, despite a peak in admissions between 2014\u0026ndash;2016, there was no significant overall change in annual admissions when considering all species together (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 2.527, df\u0026thinsp;=\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.119), suggesting that the total number of birds admitted per year remained relatively stable (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Table S3). This pattern was evident when considering diurnal raptors alone (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 0.254, df\u0026thinsp;=\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.614), however, there was a gradual decline in owl-vehicle collisions over the study period (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 11.462, df\u0026thinsp;=\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), corresponding to an approximate 3.7% decrease in admissions per year (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Table S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSpecies-specific vehicle collision admissions\u003c/h2\u003e \u003cp\u003eWhen assessing whether raptor species were admitted due to vehicle collisions more frequently than expected based on their estimated UK breeding populations, we found that some species were overrepresented among admissions relative to expectation, whereas others were underrepresented (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For example, Tawny Owl and Western Barn Owl showed significantly more admissions than expected by chance (Binomial test: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both), whereas larger raptors such as Common Buzzard were slightly overrepresented within our collision dataset (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Conversely, several species including Eurasian Sparrowhawk, Peregrine Falcon, and Red Kite did not differ significantly from expected admission rates (\u003cem\u003ei.e.\u003c/em\u003e, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eObserved and expected raptor-vehicle admissions to four wildlife rehabilitation centres in England and Wales between 2001\u0026ndash;2019. Expected number of vehicle collision admissions was calculated based on the number of admissions expected if all species experienced collisions proportional to their population size, and the \u003cem\u003eP\u003c/em\u003e value is in relation to a one-sided binomial test assessing whether each species was admitted due to vehicle collisions more often than expected by chance. \u003cb\u003eBold\u003c/b\u003e\u0026thinsp;=\u0026thinsp;species with significant number of raptor-vehicle collisions than expected by chance. *Estimates derived from the British Trust for Ornithology\u0026rsquo;s BirdFacts database.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimated number of breeding individuals in the UK*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal number of vehicle collision admissions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpected number of vehicle collision admissions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTawny Owl (\u003c/b\u003e\u003cb\u003eStrix aluco\u003c/b\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e100,000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e223\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e153\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWestern Barn Owl (\u003c/b\u003e\u003cb\u003eTyto alba\u003c/b\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e8,000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommon Buzzard (\u003c/b\u003e\u003cb\u003eButeo buteo\u003c/b\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e126,000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e224\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e193\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeregrine Falcon (\u003cem\u003eFalco peregrinus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLittle Owl (\u003cem\u003eAthene noctua\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort-eared Owl (\u003cem\u003eAsio flammeus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed Kite (\u003cem\u003eMilvus milvus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurasian Hobby (\u003cem\u003eFalco subbuteo\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurasian Sparrowhawk (\u003cem\u003eAccipiter nisus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon Kestrel (\u003cem\u003eFalco tinnunculus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMortality risk associated with road vehicle collisions\u003c/h2\u003e \u003cp\u003eRoad type was a strong predictor of mortality in raptors that had been hit by road vehicles (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 24.058, df\u0026thinsp;=\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea; Table S4). Predicted probabilities of death derived from the model indicated that raptors admitted from major roads had a probability of mortality of ~\u0026thinsp;72%, compared with ~\u0026thinsp;49% on minor roads (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In terms of odds, raptors struck on major roads had\u0026thinsp;~\u0026thinsp;2.7 times higher odds of death than those struck on minor roads, equivalent to a\u0026thinsp;~\u0026thinsp;168% increase in the odds of mortality. Contrastingly, there were no effects of season (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 1.792, df\u0026thinsp;=\u0026thinsp;3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.617) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), landscape type (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 3.819, df\u0026thinsp;=\u0026thinsp;2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.148) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), nor age class (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 0.777, df\u0026thinsp;=\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.378) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed) on raptor mortality due to collisions with road vehicles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eUsing nearly two decades of admissions data from wildlife rehabilitation centres, we provide a regional assessment of raptor collisions with road vehicles across heterogeneous rural to peri-urban landscapes in southwest England and Wales. Our findings reveal stable collision admissions over time, contrasting trends between diurnal raptors and owls, strong species-level differences in collision representation relative to population size, and a pronounced increase in mortality risk associated with major roads. Together, our results highlight how road infrastructure and species-specific ecology interact to shape collision risk and post-collision outcomes in urban landscapes, confirming findings from a recent systematic review which found that human infrastructure poses a substantial threat to raptors globally (Panter et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTemporal patterns in raptor and owl vehicle collisions\u003c/h2\u003e \u003cp\u003eDespite substantial increases in road traffic volume and continued expansion of transport infrastructure across the UK during the study period (Department for Transport 2021), we found no significant temporal trend in overall raptor admissions due to vehicle collisions. The absence of a clear increase in collision admissions over time suggests that factors beyond traffic volume alone may influence observed collision rates. For example, species\u0026rsquo; population dynamics, temporal shifts in habitat use, and changes in reporting or detection probability may contribute to stable admission numbers despite growing traffic. Similar patterns of persistence in wildlife-vehicle collision rates have been observed in other contexts. For instance, national analyses in North America found that overall wildlife-vehicle collisions did not decline in proportion to large reductions in traffic volume during the COVID-19 pandemic, indicating that collision dynamics can be decoupled from traffic trends (Abraham and Mumma \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, when analysing only owl admissions we detected a decline in incidents over the study period, driven primarily by Tawny Owl and Western Barn Owl records. This pattern may reflect documented population declines in some owl species linked to agricultural intensification, loss of rough grassland and prey availability, and secondary poisoning (Leech et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Walker et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Reduced population size would be expected to lead to fewer collision events, even if per capita collision risk remains unchanged. Alternatively, changes in road verge management or lighting regimes may disproportionately affect nocturnal species by altering foraging behaviour near roads, as anthropogenic noise and artificial light at night can reduce foraging efficiency and alter movement patterns in nocturnal animals (Senzaki et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sordello et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Disentangling these mechanisms requires integrated population monitoring alongside collision datasets, but our findings suggest that temporal trends in collision admissions should not be interpreted independently of broader population dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSpecies-specific representation in collision admissions\u003c/h2\u003e \u003cp\u003eWe found clear species-level differences in representation within the collision dataset relative to estimated UK breeding population sizes. Owls, particularly Tawny Owl and Western Barn Owl, were strongly overrepresented, whereas species such as Peregrine Falcon, Red Kite, and Eurasian Sparrowhawk occurred at or below expected frequencies within our admissions dataset. Previous research has suggested that Tawny Owls may often become victims to collisions with road vehicles due to blinding at night and their propensity to hunt and perch along roadway vegetation at night (Panter et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Western Barn Owls often fly low over field margin habitats and hedgerows while hunting at dusk (McHugh et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which may increase their risk of collision with road vehicles in agricultural landscapes (Hanmer and Robinson \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, nocturnal activity coincides with reduced driver visibility, further increasing collision risk. On the other hand, species such as Peregrine Falcon and Eurasian Sparrowhawk are aerial hunters and rarely interact with roadways when feeding, despite both species readily adapting to urban landscapes (Newton \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). The slight overrepresentation of Common Buzzards is notable given their large and expanding UK population (Stevens et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Arraut et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This species frequently scavenges carrion, including roadkill, and may be struck while feeding on carcasses or while taking off from roadside verges (Cieśluk et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This behaviour may elevate collision risk disproportionately relative to population size, particularly on rural roads where carcass removal is infrequent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRoad type as a determinant of mortality risk\u003c/h2\u003e \u003cp\u003eRoad type was the strongest predictor of raptor mortality following vehicle collisions, with birds struck on major roads experiencing substantially higher probabilities of death than those struck on minor roads. This pattern likely reflects higher vehicle speeds, increased traffic volume, and reduced reaction time for both drivers and birds on major roads (DeVault et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A study by Gagn\u0026eacute; et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported that collision risk rises with both road speed and narrowness. We observed more collisions on minor (and assumed narrower) roads, indicating that even lower-speed roads can pose high risks where road width constrains bird movement, emphasising the need to consider road geometry alongside traffic speed within mitigation planning. Although collisions occurred more frequently on minor roads, impacts on major roads were far more lethal, emphasising an important distinction between collision frequency and collision severity. Conservation practitioners should account for this distinction when prioritising locations and road types for the implementation of mitigation and collision-reduction measures.\u003c/p\u003e \u003cp\u003eThis finding aligns with studies showing that high-speed roads function as population sinks for many vertebrates, even when overall collision numbers are lower (Balčiauskas et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). From an urban ecosystems perspective, this highlights the outsized ecological impact of major transportation corridors, particularly as they intersect peri-urban and rural landscapes that support high raptor activity. Targeted mitigation measures such as verge vegetation management, carcass removal, and road-specific warning signage may therefore yield disproportionate conservation benefits if focused on major roads (Bullock et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eNo influence of season, landscape context, and age on raptor-vehicle collision mortality\u003c/h2\u003e \u003cp\u003eContrary to expectations, we detected no effect of season, surrounding landscape type, or age class on post-collision mortality. Seasonal effects on collision frequency have been documented in some raptors, \u003cem\u003ee.g.\u003c/em\u003e, Tawny Owl, often linked to juvenile dispersal or breeding activity (Raymond et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), but our results suggest that once a collision occurs, the probability of survival is largely independent of timing within the year. This may indicate that impact severity and immediate injury overwhelm seasonal or physiological differences among individuals.\u003c/p\u003e \u003cp\u003eSimilarly, the absence of a landscape effect suggests that local road characteristics and traffic conditions may be more important determinants of survival than the broader land cover context within which collisions occur. While urban, open, and closed landscapes differ in traffic density and road design, our mortality analysis indicates that these differences do not translate into consistent variation in post-admission outcomes. Age-related differences in collision susceptibility have been reported elsewhere (Orozco-Valor et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), but our findings imply that adults and juveniles are similarly vulnerable once struck, potentially due to comparable body size and trauma exposure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and implications for urban ecology and conservation\u003c/h2\u003e \u003cp\u003eAs with all rehabilitation centre datasets, our study is subject to reporting and detection biases. It is assumed that most of our admission records are of injured birds that were found but includes some individuals admitted as dead on arrival. Unfortunately, the resolution of our admissions data does not allow for this differentiation to be made, and it is therefore assumed that mortality at the scene is likely underestimated, particularly on major roads where carcasses may be rapidly removed. Additionally, population estimates used to assess species-level representation were limited to breeding individuals and did not account for non-breeders or seasonal influxes, which may influence collision exposure.\u003c/p\u003e \u003cp\u003eDespite these limitations, our study provides valuable insights into how road infrastructure shapes raptor mortality across human-modified landscapes. By integrating spatial, demographic, and infrastructural variables, we demonstrate that road type, rather than landscape context or seasonality, is the dominant predictor of post-collision survival. As road networks continue to expand globally, particularly at the urban-rural interface, understanding and mitigating their impacts on apex and mesopredators will be increasingly important for maintaining functional urban ecosystems.\u003c/p\u003e \u003cp\u003eFuture research should integrate raptor collision records with fine-scale traffic data (Heeres et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), telemetry-derived movement information (Panter et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and enhanced reporting systems, such as online citizen-science portals (Shilling et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), to improve estimates of per capita collision risk and assess mitigation effectiveness. Such multidimensional approaches will be essential for balancing transport development with the conservation of raptor populations in increasingly urbanised and fragmented landscapes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Richard Laurie and Connor Panter. The first draft of the manuscript was written by Richard Laurie and all authors commented on previous versions of the manuscript. Rachel White and Connor Panter supervised the project. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the following from various wildlife rehabilitation centres for providing admissions data: Simon Allen (Gower Bird Hospital), Elizabeth Mullineaux (Secret World Wildlife Rescue), Nikki Backhouse (Cuan Wildlife Rescue) and Carole-Ann Rose (Wild Wings Birds of Prey).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbraham JO, Mumma MA (2021) Elevated wildlife-vehicle collision rates during the COVID-19 pandemic. Sci Rep 11:20391. https://doi.org/10.1038/s41598-021-99233-9\u003c/li\u003e\n \u003cli\u003eArraut EM, Walls SW, Macdonald DW, Kenward RE (2021) Anticipation of common buzzard population patterns in the changing UK landscape. Proc Biol Sci 288:20210993. https://doi.org/10.1098/rspb.2021.0993\u003c/li\u003e\n \u003cli\u003eBalčiauskas L, Kučas A, Balčiauskienė L (2025) A review of wildlife\u0026ndash;vehicle collisions: a multidisciplinary path to sustainable transportation and wildlife protection. Sustainability 17:4644. https://doi.org/10.3390/su17104644\u003c/li\u003e\n \u003cli\u003eBates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Soft 67:1\u0026ndash;48. https://doi.org/10.18637/jss.v067.i01\u003c/li\u003e\n \u003cli\u003eBoal CW, Dykstra CR (2018) Urban Raptors, Ecology and Conservation of Birds of Prey in Cities. Island Press Washington, DC, USA.\u003c/li\u003e\n \u003cli\u003eBuij R, Richards NL, Rooney E, Ruddock M, Horv\u0026aacute;th M, Krone O, Mason H, Shorrock G, Chri\u0026eacute;l M, De\u0026aacute;k G et al (2025) Raptor poisoning in Europe between 1996 and 2016: a continental assessment of the most affected species and the most used poisons. J Raptor Res 59:1\u0026ndash;19. https://doi.org/10.3356/jrr2373\u003c/li\u003e\n \u003cli\u003eBullock H, Panter CT, Miller TA (2024) Conservation Letter: Raptor collisions in built environments. J Raptor Res\u003cem\u003e\u0026nbsp;\u003c/em\u003e58:396\u0026ndash;406. https://doi.org/10.3356/jrr248\u003c/li\u003e\n \u003cli\u003eButet A, Michel N, Rantier Y, Comor V, Hubert-May L, Nabucet J, Delettre Y (2010) Responses of common buzzard (\u003cem\u003eButeo buteo\u003c/em\u003e) and Eurasian kestrel (\u003cem\u003eFalco tinnunculus\u003c/em\u003e) to land use changes in agricultural landscapes of western France. 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J Wild Disease 38:101\u0026ndash;106. https://doi.org/10.7589/0090-3558-38.1.101\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"urban-ecosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ueco","sideBox":"Learn more about [Urban Ecosystems](https://www.springer.com/journal/11252)","snPcode":"11252","submissionUrl":"https://submission.nature.com/new-submission/11252/3","title":"Urban Ecosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"birds of prey, threats, urbanisation, raptors, road ecology","lastPublishedDoi":"10.21203/rs.3.rs-8997953/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8997953/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRoad expansion represents a growing threat to biodiversity, particularly within urban and peri-urban landscapes where wildlife movements intersect with transportation networks. Raptors are especially vulnerable to vehicle collisions due to their hunting behaviour and use of roadside habitats, yet empirical evidence from the UK remains limited. Here, we use admissions data from wildlife rehabilitation centres (WRCs) in England and Wales to examine patterns and outcomes of raptor-vehicle collisions. We analysed 586 geo-referenced admissions involving 10 raptor species between 2001\u0026ndash;2019, assessing how species, road type (minor vs. major), season, landscape type and age influence mortality outcomes. Most collisions occurred on minor roads, however, collisions on major roads were substantially more lethal. Raptors struck on major roads had approximately 2.7 times higher odds of mortality than those struck on minor roads, highlighting an important distinction between collision frequency and collision severity. Relative to their breeding population sizes, Tawny Owl (\u003cem\u003eStrix aluco\u003c/em\u003e), Western Barn Owl (\u003cem\u003eTyto alba\u003c/em\u003e) and Common Buzzard (\u003cem\u003eButeo buteo\u003c/em\u003e) were disproportionately more likely to be admitted to WRCs following collisions with road vehicles than expected by chance. Raptor-vehicle collisions remained relatively stable, highlighting the persistent threat that roadways pose for raptors over time. This study provides the first multispecies assessment of raptor-vehicle collisions across road networks in the UK. Our findings emphasise the disproportionate impact of major roads on raptor survival and highlight the need for targeted mitigation actions, including verge management and strategic speed reductions, within urban and peri-urban ecosystems.\u003c/p\u003e","manuscriptTitle":"Major roads drive higher raptor mortality: insights from long-term rehabilitation data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 09:27:34","doi":"10.21203/rs.3.rs-8997953/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-08T13:53:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T18:18:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T18:07:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298941248678287732912759154976671123106","date":"2026-03-11T16:17:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222525152682643648385866068076524135563","date":"2026-03-11T16:06:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-09T15:15:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T03:21:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-03T10:57:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Urban Ecosystems","date":"2026-02-28T21:08:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"urban-ecosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ueco","sideBox":"Learn more about [Urban Ecosystems](https://www.springer.com/journal/11252)","snPcode":"11252","submissionUrl":"https://submission.nature.com/new-submission/11252/3","title":"Urban Ecosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1ba9bc70-09e1-48ba-b9cc-466af37bd5a0","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-08T13:53:49+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T14:10:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 09:27:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8997953","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8997953","identity":"rs-8997953","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}