The distribution of vertebrate roadkill varies by season, surrounding environment and animal class

doi:10.21203/rs.3.rs-2246792/v1

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The distribution of vertebrate roadkill varies by season, surrounding environment and animal class

https://doi.org/10.21203/rs.3.rs-2246792/v1

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published 03 Apr, 2023

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Due to rapid human expansion in the last century, wildlife roadkill is becoming a concerning threat to biodiversity and human safety. The frequency of roadkill events depends on factors related to specific traits of the road –fencing, tortuosity, or type of habitat, among others– and the animal ecology –such as activity patterns, reproductive season, thermoregulation– which, in turn, are related to environmental factors (with seasonal variations). Here we assessed roadkill mortality of terrestrial vertebrates over the year. To do this, we sampled 10 road sections (of 3 km, by walk) in the south of Spain for a full year, registering the carcasses of run-over vertebrates. Then, we analysed the spatiotemporal patterns of roadkill events for the four vertebrates’ classes and the effects of road traits (presence of fence, tortuosity, distance to water body) and environmental variables (mean temperature and precipitation). Mammals suffered the highest mortality by roadkill (45.72%). Tortuosity of the road section and precipitation were not related to the number of collisions, while the presence of fences was related to an increase in roadkill events, and mean temperature significantly increased the probability of collision of mammals, birds, and reptiles. There was a seasonal effect in the number of collisions, which spatial pattern depended on the class of vertebrates. All this leads us to conclude that, to reduce the impact caused by roadkill mortality on the wildlife we need specific measures to be taken in each critical place for each vertebrate group.

animal-vehicle collisions

road ecology

wildlife management

mammals

roadkill mortality

In the last century, the development of human societies has increased with an unprecedented magnitude and speed, which has led to global changes in wildlife (Barrientos et al. 2021). Populations of most animal species are decreasing (Jackson and Fahrig 2011). The main factors impacting the population density of animals are land-use modifications, pollution, climate change and building infrastructures that fragment the habitats (Towers et al. 2016). Among those infrastructures are roads, which show one of the highest densities in Europe (Meijer et al. 2018), to an extent that 50% of the continent’s land is less than 1.5 km from some road (Torres et al. 2016). These infrastructures entail changes in land use and habitat fragmentation, which hinders the flow of animals among subpopulations within a metapopulation, reducing the viability of the populations (Benítez-Lopez et al. 2010). Moreover, collisions between the vehicles that cross these roads and wildlife that try to make their way through them are one of the main causes of wildlife mortality worldwide (Hill et al. 2019).

The frequency of animal-vehicle collisions depends on different factors related to the type of animal, environmental factors, and traits related to the road itself. Two important factors related to the species are motility (D'Amico et al. 2015) and the ability to avoid or escape from vehicles, with animals that run less often –as amphibians– being more likely to be run over than others with a greater capacity of reaction to a vehicle –such as birds or some mammals (Mazerolle et al. 2005). Within an animal group, the risk of being hit will depend on the time of year, either because the species has different requirements depending on the time of year, as is the case of the dependence of bodies of water in some amphibians and birds (Gibbs et al. 2005), or due to the variation of the activity periods, as may be the case of some reptiles with brumation, some mammals with hibernation, or amphibians with aestivation (Garriga et al. 2017). In the latter, the probability of being run over increases in rainy periods and, particularly, after sunset, when they reach their maximum activity (Gibbs et al. 2005). In addition, the effects of weather conditions on roadkill risk can also be indirect, for example, by influencing the presence of possible prey on roads (Barrientos and Bolonio 2009). Considering that different variables can affect the risk of roadkill for each vertebrate group, it is necessary to determine their impact on natural populations (Roger et al. 2011). This means that the impact suffered by a very abundant species with a high reproductive capacity is not the same as that suffered by one that is very endangered and has a low reproductive rate, and this impact will also depend on the developmental stage of the individual that has been hit (Row et al. 2007).

Here we intended to understand the variables related to the seasonal spatiotemporal patterns of roadkill of terrestrial vertebrates. Our first aim was to assess the degree of roadkill mortality of each vertebrate class (amphibians, reptiles, birds, and mammals) and identify the species that were most affected. Since they show the greatest mobility, we predict that mammals will be the group suffering the greatest number of collisions. Secondly, we aimed to understand how some traits of the road (tortuosity, presence of fencing, type of surrounding habitat, and distance from a body of water) affect the frequency of accidents. The presence of fencing should reduce the number of accidents, at least for mammals; the tortuosity would also reduce the number of accidents, since vehicles reach higher speed on straight road sections; the distance to a body of water could be related to a greater run over of amphibians and birds; and for the type of habitat, habitats harbouring larger abundance of animals would propitiate higher mortality. Third, we aimed to explore the seasonal fluctuations on roadkill and its variation between different groups of vertebrates. We hypothesise that seasonal variations on roadkill will depend on the biology of the different vertebrate groups, with reptiles suffering a greater number of road accidents in warm months, amphibians being most run over on periods with greater precipitation and humidity and birds and mammals showing little or no seasonal effect. Finally, we aimed to evaluate the relationship of the main environmental variables with the accidents, for each class of vertebrates. The effect of these variables will also vary among vertebrate groups, with reptiles increasing roadkill with higher temperatures, while that of amphibians could be related more to greater precipitation.

2.1. Study site and road survey

Along a complete year, we sampled, by walk, 10 road sections within two municipalities in southern Spain: Loja (Granada) and Archidona (Málaga) (Fig. 1). We surveyed the road sections for 12 months –starting in March 2021 and ending in February 2022– during the last weeks of each month. All road sections had the same length (3 km) and similar speed limits (≈ 60 km/h throughout their length). Road sections differed on some traits, such as the type of surrounding habitat, the sinuosity, and the presence of fencing (5 sections fenced and 5 unfenced).

Each month, the same researcher (JAR) walked the 10 road sections on foot. For each sampling of each road section, a round trip walk was carried out, paying attention to the left lane in each round (including the lane itself, the hard shoulders, gutters, and culverts). Thus, the sections were always covered on the left of the road, with a reflective vest to reduce the risk of an accident. Sampling focused exclusively on terrestrial vertebrates (mammals, birds, reptiles, and amphibians). Each run-over carcass was registered using the mobile application ObsMapp (González et al. 2018), including: species (whenever it was possible to identify it; otherwise, we recorded the lowest taxonomic level identifiable), date and time of the observation, GPS coordinates and photography. Data were later uploaded to the observation.org platform for its subsequent use in the national SAFE program, aiming to reduce roadkill mortality (Caballero-Díaz et al. 2021). On this platform, carcasses’ pictures are reviewed by the scientific community to confirm species identification. Once a carcass was registered, it was removed from the road to avoid possible additional accidents by potential predators.

2.3. Data analysis

We analysed the effect road traits (road sinuosity and presence of fence), and habitat traits (type of habitat, distance to a water body, mean temperature and mean precipitation) on the spatiotemporal patterns of roadkill events of the four vertebrate classes (amphibians, reptiles, birds, and mammals) along seasons of the year.

The study area has a Mediterranean climate with four marked seasons, that we categorised as: (1) spring, from March 20 to June 21, (2) summer, from June 21 to September 23, (3) autumn, September 23 to December 21, and (4) winter, from December 21 to March 20. In addition, the road segments are located in different four habitat categories: (1) holm oak, (2) olive groves, (3) broom brushes, and (4) mixed (a mixture of holm oak, olive grove, broom, crop fields, etc.).

We used QGIS version 3.16 (QGIS Development Team 2022) to quantify road sinuosity and distance to water bodies. To estimate the sinuosity or tortuosity of each road section, we digitised the road section as a linear shapefile type layer to measure (1) its exact length and (2) the Euclidean distance between the start point and the end point of the section and estimate the tortuosity simply as the quotient between both distances (i.e., total distance / [start point - end point]). The higher the value, the greater the tortuosity of the section of the road in question. To calculate the distance of each carcass to the nearest body of water, these were first digitised as a polygon shapefile-type layer, visually identified with the Google Hybrid plugin, selecting natural ponds and lagoons, irrigation ponds of apparently natural shores, and ignoring pools or irrigation ponds contained in walls. Once the bodies of water had been digitised, we used the QGIS "distance to the nearest axis" tool to calculate the distance between each carcass and the closest limit to a water body.

In addition, we obtained six environmental variables from the closest meteorological station (Loja’s Meteorological Station) at a monthly scale: maximum temperature (T_max), minimum temperature (T_min), mean temperature (T_mean), mean radiation (R_mean), mean precipitation (P_mean) and mean humidity (H_mean).

All data were exported to a global database and analyses were conducted on R programming language (R Core Team 2021). To analyse whether the number of accidents depended on road tortuosity, type of habitat and presence of fencing (i.e., characteristics own of the road), we fitted a Poisson GLM, including the main effects of the three explanatory variables. We also analysed whether fencing differentially affected roadkill mortality according to the four classes of vertebrates; for this, a Poisson GLM was adjusted on the number of roadkill events, with the class (amphibians, reptiles, birds, mammals), the presence of fencing (yes or no), and its interaction as fixed terms. To analyse whether the percentage of roadkill events differed for each animal class and season of the year, we fitted a Chi-square test. To delve into the effect of the season on roadkill mortality of the different classes of vertebrates, we fitted a Generalized Linear Mixed Model (GLMM) linked to a Poisson distribution on the number of collisions, with class (amphibians, reptiles, birds, mammals), season (spring, summer, autumn, winter) and their interaction as fixed terms and the road section as a random factor (random intercept), using the lme4 package (Bates et al. 2011). Lastly, we analysed the effect of climatic variables on the probability of the hit animal being an amphibian, reptile, bird, or mammal, we fitted a multinomial logistic regression model using the nnet package (Ripley et al. 2016). The six weather variables (T_max, T_min, T_mean, R_mean, P_mean, and H_mean) were highly correlated (r with an absolute value ≥ 0.79 for all bivariate correlations, except those related to precipitation; see correlation plot in Online Resource 1), so we decided to use only T_mean and P_mean (Pearson's correlation, r = -0.48) as explanatory variables in the multinomial logistic regression model. For all adjusted models, we verified that residuals followed an approximately normal distribution.

Given that the distance of the carcass to the nearest water body was not normally distributed and the variances are very heterogeneous between the different classes of vertebrates, we used a Kruskal-Wallis’ test to analyse the differences in the distance to the water by class, followed by paired comparisons using Mann-Whitney U tests with Bonferroni’s corrected p-values. To visualise the spatial distribution of roadkill events across vertebrate taxa and seasons, we obtained heatmaps of run-over animals in the study area for each animal class and season of the year. For plots and maps, we used the packages ggplot2 (Wickham 2011), raster (Hijmans et al. 2015), maptools (Bivand et al. 2022), ggmap (Kahle et al. 2019) and sp (Pebesma et al. 2012).

We registered a total of 413 vertebrate carcasses, 26 amphibians (6.30%), 62 reptiles (15.01%), 136 birds (32.93%) and 189 mammals (45.76%) (Fig. 2A). Roadkill mortality varied throughout the year for the different classes of vertebrates (Fig. 2B). Among amphibian species, the most run over was the natterjack toad (Epidalea calamita) (n = 10), in reptiles the Montpellier snake (Malpolon monspessulanus) (n = 18), in birds the common blackbird (Turdus merula) (n = 21) and in mammals the European rabbit (Oryctolagus cuniculus) (n = 73) (see Online Resource 2 for the full list of carcasses).

The model aimed at evaluating the possible effect of road tortuosity, type of habitat and presence of fencing revealed that the number of collisions varied depending on the habitat in which the road section is located (Fig. 3A), increased in the fenced sections (β_Fencing = 0.804, p = 0.001) and was independent of road tortuosity (β_Tortuosity = -0.236, p = 0.546). Road sections crossing mixed habitats registered significantly more mortality than those in holm oak forests (the reference category) (β_Mixed = 0.356, p = 0.007), and those crossing broom brushes showed less mortality (β_{Broom−brushes} = -0.894, p < 0.001), with no differences on mortality between oak and olive groves (β_{Olive−groves} = -0.103, p = 0.677) (Fig. 3A).

In the case of fencing, we fit another model to investigate whether the effect of fencing was the same for the different classes of vertebrates. The significant interaction showed that the effect of fencing varied for the four classes of vertebrates (Fig. 3B), with an increase in collisions in fended road sections for reptiles (β_Reptiles = 1.202, p = 0.012), birds (β_Birds = 1.332, p = 0.003) and mammals (β_Mammals = 1.583, p < 0.001) compared to amphibians (our reference category) (Fig. 3B).

There was a significant association between the season of the year and the animal class (Chi² = 33.37, d.f. = 9, p < 0.001) (Fig. 4A). To assess the effect of the season on each class of vertebrates, we fitted a mixed GLM with a Poisson error distribution family and the road section as a random factor. The model did not show any significant interaction between the season of the year and the animal class (Table 1).

Table 1

Results of the mixed GLM with Poisson family distribution errors, with the number of carcasses as the response variable, the main effects of the animal class (amphibian, reptile, bird, or mammal) and the study season (spring, summer, autumn, or winter), as well as their interaction, as fixed factors, and the road section as a random factor. Amphibians and spring were considered as reference levels. Significant results are indicated in bold
Term	Coefficient	Z	p-value
Intercept	0.516	1.443	0.149
Class (reptiles)	0.356	0.908	0.364
Class (birds)	0.895	2.410	0.016
Class (mammals)	1.197	3.333	0.001
Season (summer)	-0.806	-1.026	0.305
Season (autumn)	-0.757	-1.131	0.258
Season (winter)	0.133	0.301	0.763
Class (reptiles) × Season (summer)	0.960	1.152	0.249
Class (birds) × Season (summer)	0.832	1.018	0.309
Class (mammals) × Season (summer)	0.881	1.093	0.274
Class (reptiles) × Season (autumn)	0.421	0.537	0.592
Class (birds) × Season (autumn)	0.300	0.417	0.677
Class (mammals) × Season (autumn)	0.273	0.390	0.697
Class (reptiles) × Season (winter)	-0.835	-0.969	0.333
Class (birds) × Season (winter)	-0.013	-0.026	0.980
Class (mammals) × Season (winter)	-0.688	-1.396	0.163
AIC = 503.5; BIC = 549.1, deviance = 469.5, d.f. residuals = 91 (108 observations, 10 groups). Random effects: variance = 0.151, standard deviation = 0.388

Distance of the carcass to a body of water differed by class (Kruskal Wallis test, Chi² = 22, d.f. = 3, p < 0.001) but pairwise comparisons (Mann-Whitney U tests with Bonferroni-corrected p-value) indicated that only bird carcasses were closer to water masses than mammal ones (W = 9020, p < 0.001) (Fig. 4B). The number of collisions varied during the year with the oscillations of the environmental variables –mean temperature, relative humidity, and solar radiation– (Fig. 5). It showed that the probability of being run over was not related to precipitation (p > 0.05 in all cases), while the mean temperature significantly affected the probability of being run over (β_Reptiles = 0.253, p < 0.001; β_Birds = 0.099, p < 0.001; β_Mammals = 0.107, p < 0.001; amphibians is the reference category) with different trends for each vertebrate class (Fig. 4C).

The spatial exploration of roadkill mortality showed a great seasonal variability in the different road sections for the different animal classes, allowing the identification of critical points for each group (Fig. 6).

By monthly sampling of 10 road sections along a whole year, we recorded 413 carcasses of run-over vertebrates, 45.76% of them were mammals, 32.93% birds, 15.01% reptiles and 6.30% amphibians. Our annual evaluation of roadkill mortality lets us better understand spatiotemporal patterns for each group and the variables related to them. While road sinuosity and precipitation did not affect the frequency of accidents, we found an effect of the type of habitat, fencing, season, and environmental temperature on roadkill mortality. Nonetheless, some of these effects varied among animal groups. However, the effect of season on roadkill mortality of each animal class depends on the studied road section, indicating that specific actions must be taken to target animals at critical sites and moments of the year.

We found that roadkill mortality varies throughout the year, with different patterns for each vertebrate class, the peak of collisions coinciding with suitable environmental conditions for each group, when they are most active (Garriga et al. 2017). Of the 413 carcasses recorded, mammals were the most run over, with 189 animals killed on the road (45.76% of the total). Among mammal’s carcasses, 73 were European rabbits (Oryctolagus cuniculus), which represents 17.68% of all dead vertebrates registered in the study (Online Resource 2). This soaring roadkill mortality of rabbits may be due to different factors, such as the mobility of this species, higher than that of other vertebrates (D'Amico et al. 2015), the proximity of their burrows to the roads –since the loss of their natural habitats pushes them to construct burrows on ditches and roadsides– may also contribute to the problem (Gea-Izquierdo et al. 2005), or just simply the fact that it is a highly abundant species in the studied habitats. In any case, the high mobility of European rabbits and the proximity of their burrows to roads is concerning, since they are one of the main prey of carnivores in the Mediterranean area (Bautista et al. 2004; Barrientos and Bolonio 2009) and can attract carnivores or scavengers to the roads (Birks and Kitchener 1999), increasing their risk of collision with vehicles.

The low number of amphibian roadkill also stands out, which could be due to various causes. In the first place, detectability, since sampling has always been carried out during the daytime, and the carcasses of animals run over at night may not remain on the road the next day. As the period of greatest activity of amphibians is at night, and especially when precipitation and humidity are high (Gibbs et al. 2005), it is possible that at the time of sampling, either the carcasses have been run over by a lot of cars and are visible anymore or have been predated (Mazerolle et al. 2005). In addition, the study year was dry for the area (only 187 mm³ of precipitation), which could reduce the activity of these vertebrates and, consequently, their roadkill mortality (Jakob et al. 2003). Looking ahead to future studies, specific nocturnal sampling should be carried out to better determine the impact of road accidents on amphibians.

Regarding the type of habitat, the mixed habitat showed the greatest number of accidents, significantly more than in holm oak forests, and broom brushes showed the lowest number of accidents. These results could be explained due a different abundance of animals according to the surrounding habitat, but we have not enough information to test this idea. Future studies on the impact of habitat on roadkill will benefit from censuses in the surrounding habitat to test for differences in animal abundance. Also, animal movements may differ among habitats, so studies on how animals move in different habitats would be needed to check the impact of roads. Habitat also could influence vehicle speed, and greater vehicle speed increases the probability of collision (Van Langevelde and Jaarsma 2004; Mazerolle et al. 2005). Since we focused on seasonal and animal group effects, we only studied 10 road sections. Thus, to clarify the influence of the type of habitat on vertebrate roadkill mortality, we need specific studies with sufficient replicas in each type of habitat and quantify animal abundance and flow of traffic and speeds reached by vehicles.

Fences, contrary to what we expected, increased the number of collisions of mammals, birds, and reptiles, compared to sections without fencing. These results, a priori surprising, are explained once we observe the type and condition of the fence. Firstly, the vast majority of road has a generalist fence, which only impedes the crossing of large mammals, allowing micro and mid-mammals, as well as reptiles and amphibians, to cross it easily (Dodd et al. 2004). In addition, the fencing at most points of the study roads was in poor condition (pers. obs.), either cut or bent, causing it not to fulfil its function, and even acting as a funnel through which most vertebrates must pass (van der Ree et al. 2015). As the five fenced and five unfenced sections were in different habitats, it is also possible that habitat type affected the amount of roadkill events, rather by influencing the density of vertebrates in adjoining habitats (Klocker et al. 2006) or the traffic flow that passes through these roads (Van Langevelde and Jaarsma 2004). Therefore, we recommend carrying out more specific studies to be able to determine the role of fencing in the number of collisions, as well as in the fragmentation of the populations of the different groups of vertebrates.

The general analysis showed a relationship between the season and the frequency of collisions of each class of vertebrates. However, when we fit a model including the road segment as a random factor, we see that there is no significant seasonal effect on the number of roadkill events of any of the vertebrate groups. This gives us an idea about the relevance of the variation in the number of collisions for each group of vertebrates between road segments (as we see in Fig. 6) so specific studies should be carried out to detect the critical collision hotspots of each group along the year. Overall, seasonal variations are important, but it seems almost more important to identify the critical hotspots and moments of each class of vertebrates for collision prevention.

About the distance to water bodies, we only found a significantly shorter distance for bird carcasses in relation to mammals. Most mammal species that we have found run over are typically Mediterranean species, that is, adapted to summer drought. In addition, intense drought experienced in 2021 (Pratt 2022) prevented most ponds from being filled during the study period. It is possible that mammals use temporary ponds and streams for hydration, so they are not necessarily associated with large perpetual bodies of water (Sánchez-Montoya et al. 2017). In the case of birds, the greater proximity of accidents to water masses was expected, since most species depend on water bodies, so they should preferably be located close to them (Pšeničková and Horák 2022). The road section where we found a greater number of accidents with birds (Fig. 1, Fig. 6) is close to rivers and ponds.

Although the seasonal effect on the number of collisions varies by section, there are certain climatic conditions related to them, as is the case of the average temperature that significantly affected the probability of being run over for mammals, birds, and reptiles. However, precipitation did not show an effect on the probability of being hit by the different vertebrate classes. This, again, could be due to the scarcity of precipitation in the study area and period, which could have been even below a threshold from which we could find effects. Despite this, we can see that the number of amphibians being run over shows a certain relation with the increase in relative humidity and the decrease in average radiation (Fig. 5), as was found in other studies (Jakob et al. 2003). For mammals, the peak probability of being run over was around 15 ºC, which is consistent with the temperatures in which most mammals show greater activity (Villafuerte et al. 1993, Zanni et al. 2021). For birds, the peak probability of being run over was around 12 ºC, also a suitable temperature for activity (Khaliq et al. 2014). Meanwhile, this peak was around approximately 27 ºC for reptiles, which coincides with temperatures in late spring and summer, where periods of greater activity of this group occurs (Fig. 4C) (Ortega and Marín-Vallejo 2019). Reptiles use roads as basking surfaces or to move from one side of the road to the other in search of food or shelter (Fahrig and Rytwinski 2009), which increases their risk of being run over there.

Exploring the distribution of the accidents (maps in Fig. 6) we see that, first, amphibian roadkill mainly occurs at specific points within each road section, since these animals live near bodies of water (Jakob et al. 2003). For example, at the beginning of the road section 9, we found several carcasses around the same point (37.071302, -4.287494), which is an outcrop of water that accumulates there and is distributed along road section 8 through irrigation ditches. For reptiles, we find most accidents concentrated at road sections 1 and 3, both in oak forests, an optimal habitat for this group (Vignoli et al. 2017). For birds and mammals, we observe a more homogeneous distribution of accidents in all road sections. One can see that a large part of the crashes occurs in the straight sections of the road, in which the vehicles may reach a higher speed, reducing both the driver's and the animals’ reaction capacity (Van Langevelde and Jaarsma 2004). Although more specific studies are needed to determine the specific causes of collisions for each group, we assessed the seasonal patterns of roadkill mortality for each vertebrate class in this Mediterranean area and the environmental and road-related associated traits, and we were able to identify critical points to intervene for each group. We showed that even in a small study area, the spatiotemporal collision patterns of amphibians, reptiles, birds, and mammals are highly variable between road sections, which pinpoints the necessity of local studies to reduce wildlife roadkill and increase human safety on the roads.

4.1 Recommendations to reduce roadkill mortality

Among prevention actions, some of them are general and some are group specific. Fencing is one of the most used, but its efficiency in preventing animals from accessing the road depends on the size and mobility of the animal species. For example, a fence designed for large mammals will not prevent reptiles from crossing the road. But some adaptations can make a general fence to work for more than one animal group, for example, adding a metal plate embedded in the ground in a horizontal position will prevent the passage of amphibians, reptiles, and small mammals, among others (van der Ree et al. 2015). A highly effective fencing will have a counterpart since it may increase the fragmentation of habitats (Pascual-Hortal et al. 2006) and so reduce the viability of animal populations (Glista et al. 2009). However, identifying the spatiotemporal roadkill patterns of each group can help us maximise fencing benefits and reduce its costs. Once we identify roadkill mortality hotspots for each group, we can promote policies to adapt fences at these points and –contrary to what we currently found in the study area– promote fencing maintenance, so they can fulfil their function.

Another measure is to limit the vehicle’s speed (Van Langevelde and Jaarsma 2004) and/or its access to certain sections in certain periods, for example, areas where amphibians frequently pass by during rainy nights. Speed can be controlled by radars, bumps or other structures that force drivers to reduce their velocity and should be reinforced by awareness raising and education actions. For example, the SAFE plan in Spain involves people through a large citizen project in which anyone can collaborate to assess and reduce roadkill mortality (Caballero-Díaz et al. 2021).

Conclusions from the present study indicate that the type of measure must be adjusted to the specific place and group, which will maximise its effectiveness and reduce its negative effects. For example, in critical points of collision with mammals, it makes sense to set specific fencing for this class, reducing the impact it may have on the other groups. Thus, it is possible to act with different measures for each group, in addition to including radars and specific signage at each critical point.

Conflict of interests

The authors declare no competing interests.

Funding

Zaida Ortega was funded by a postdoctoral talent-attraction contract from the Junta de Andalucía, co-funded with European Commission funds.

Author contributions

Conceptualization: JAR, ZOD, GMR; Data collection: JAR; Data analysis: ZOD, JAR; Writing - original draft preparation: JAR; Writing - review and editing: ZOD, GMR; Supervision: ZOD.

Acknowledgments

We thank our colleagues from the Evolutionary Ecology of Mediterranean Fauna lab for their suggestions and support. We also thank Alberto García-Rodríguez, Miguel Clavero and Eloy Revilla for the information about the SAFE project. Zaida Ortega was funded by a postdoctoral talent-attraction contract from the Junta de Andalucía, co-funded with European Commission funds (Ref. 401).

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No competing interests reported.

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The distribution of vertebrate roadkill varies by season, surrounding environment and animal class

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Abstract

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1. Introduction

2. Materials And Methods

2.1. Study site and road survey

2.3. Data analysis

3. Results

4. Discussion

4.1 Recommendations to reduce roadkill mortality

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1