# 1. On the value of citizen science for biodiversity monitoring

The challenges associated with roadkill monitoring, discussed in the introduction of this manuscript, are also relevant to all forms of biodiversity monitoring, where assessing the size and health of wild populations can prove costly and difficult (Schmeller et al., 2009). In response to these challenges, tools from citizen science tools have been explored as promising methods for data collection, including the collection of roadkill data. One notable example of the use of volunteer contributors to inform wildlife presence and characteristics is the Cornell Lab of Ornithology: for over 60 years, they have enlisted the help of volunteers to monitor bird species' presence, reproductive success and habits, while educating the public on the conservation issues faced by these species (Bonney et al., 2009; Dickinson, 2011). According to Donnelly et al. (2014), the earliest example of volunteers contributing to the data gathering process dates back to the 1900 (Christmas bird count, United States). Over the years, projects carried out by volunteers have yielded valuable information on species distributions (Klemann-Junior et al., 2017), the impact of global change on ecosystems (Larson et al., 2020), the monitoring of rare or elusive species (Robinson et al., 2018), and have helped to evaluate the efficiency of biodiversity conservation measures (Pellissier et al., 2020). These datasets may also hold value for decision-making in conservation (Newman et al., 2017). Therefore, the results presented in this manuscript have implications that extend beyond the specific issue of wildlife road mortality.

The challenges associated with working with biodiversity data collected through citizen science projects are quite extensive: erroneous reports, variations in species detectability, and biased selection of both sampling locations and species to report (Johnston et al., 2023). Opportunistic reporting is perhaps the most efficient method for collecting large quantities of data, but the presence-only data generated by this method presents significant challenges when it comes to modelling, compared to standardized surveys (Mengersen et al., 2017; Ver Hoef et al., 2021). Despite proving a valuable source of data in a number of occasions (Hall et al., 2021; Henckel et al., 2020; Klemann-Junior et al., 2017; Tiago et al., 2017), opportunistic citizen science data is sometimes regarded by authors as unreliable but yielding large datasets, while on the other hand, standardized sampling conducted by researchers is considered to be of higher quality, albeit with smaller datasets (Couvet et al., 2008; Elliott & Rosenberg, 2019). The existence of this trade-off between quantity and quality may be perceived as an inevitable outcome of involving volunteers in the data collection process. However, studies have shown that citizen science data can be effectively rectified to produce high-quality data (Bird et al., 2014; Cretois et al., 2021; Isaac et al., 2014; Robinson et al., 2018), a process which we have explored here in the context of opportunistic roadkill datasets.
Finally, one of the main challenges in managing citizen science monitoring schemes is to ensure the continued implication of its contributors, as the sampling effort is directly linked to the number of volunteers (Schmeller et al., 2009). Hsu & Lin (2021) showed that participants' motivations for taking part in such projects included the desire to contribute to scientific research, and that they may stop contributing in the project when they do not experience this sense of achievement (see also Thompson et al., 2023). Opportunistic monitoring schemes should therefore produce scientific knowledge in order to retain their contributors. In the case of Faune-AuRA, neither the roadkill dataset nor the larger species' presence dataset have been mentioned or used in scientific studies. The work that is presented in this manuscript can therefore potentially be the start of a virtuous cycle, where the recognition of the value of the data produced by volunteers, both through scientific publications but also public restitutions, can help ensure that the number of contributors remains sufficient for future contributions of the Faune-AuRA project to scientific knowledge and decision-making in conservation. In consequence, I have tried to during this project to ensure that the public, and in particular the project contributors, had access to the new knowledge produced, instead of focusing solely on publishing in peer-reviewed journals.

## 1.1. How biased are datasets from opportunistic roadkill monitoring?

We have explored three sources of bias in citizen science opportunistic roadkill datasets: roadkill persistence, probability of detection and probability of reporting. Citizen science monitoring schemes usually cover large spatial scales (Swinnen et al., 2022; Waetjen & Shilling, 2017) and operate in continuous time. In consequence, detecting spatio-temporal structures in these different sources of bias is also a major concern. While we have not been able to explore all the nuances in these sources of bias over the duration of this project, we have a better understanding of the roadkill reporting process in opportunistic citizen science, and can rely on existing literature to provide further insights on the subject (Table 1).
<table><caption>Table 1: Summary of the potential sources of bias present in opportunistic roadkill datasets and the factors influencing them. A green cell indicates that we have investigated the component and found a significative impact, a red cell indicates that we did not find any impact. White cell represent factors we did not cover in this project (green text: effect demonstrated by other authors, red text: no effect found in other studies, black text: hypothesis that remains to be investigated). The last column summarizes the estimates we have obtained ($T_R$ = characteristic persistence time sensu Teixeira et al. (2013); $P_{\text{detection}}$ = roadkill detection probability; $P_{\text{report | detected}}$= reporting probability of detected roadkill by Faune-AuRA project contributors).</caption><thead><tr><th></th><th></th><th>Spatial</th><th>Temporal</th><th>Biological traits</th><th>Estimates (95% CI)</th></tr></thead><tbody><tr><td rowspan="4">Persistence</td><td>Amphibians (anurans) (~35g)</td><td>Road traffic (100 – 8000 veh.day<sup>-1</sup>)</td><td>Rainfall</td><td>Body mass (Brzeziński et al., 2012)</td><td>T<sub>R</sub> = 24h30 (13h10,45h57) - 36 min (12 min,1h18)</td></tr><tr><td>Small passerines (~20g)</td><td>Road traffic</td><td>Rainfall</td><td>Body mass?</td><td>T<sub>R</sub> = 40 min (13 min, 1h07)</td></tr><tr><td rowspan="2">Birds and mammals (20g – 25kg)</td><td>citizen science</td><td>Road traffic</td><td>Season</td><td rowspan="2">Body mass</td><td>T<sub>R</sub> = 59h (46h,149.5h)</td></tr><tr><td>manuscript n°3</td><td>Road traffic</td><td>Time of day (Slater, 2002)</td><td>T<sub>R</sub> = 40h37 (10h37,90h27)</td></tr><tr><td>Detection</td><td>Birds and mammals (160g – 15kg) citizen science context</td><td>Vehicle speed (50 – 90km.h<sup>-1</sup>)</td><td>Position of the sun (Collinson et al., 2014)</td><td>Body size (Eurasian Jay – European badger)</td><td>P<sub>detection</sub> = 9% (6.5,11.4) -20.6% (15.7,25.5)</td></tr><tr><td>Reporting</td><td>Birds and mammals (500g - 15kg)</td><td>Influence of road traffic and configuration?</td><td>-</td><td>Positive bias toward charismatic species?</td><td>P<sub>report | detected</sub> = 33.5% (5.4,72.2)</td></tr></tbody></table>

We assessed for the first time the *search efficiency* (i.e., detection probability) of drivers in the context of citizen science, where observers are not trained professionals and opportunistically report roadkill (i.e., they are not likely to be actively searching for roadkill while driving). We showed that detection probabilities are much lower in this context than during traditional road surveys (manuscript n°4). Focusing on the size of the animal as a continuous predictor of roadkill detectability (Barrientos et al., 2018), we constructed a model useful for predicting the detection probabilities of a wide range of species, both in the context of citizen science (e.g., roe deer ~ 25kg: $P_{\text{detection}}$= 26.7%, 95% CI : 17.6%, 40.3%; small passerine ~ 20g: $P_{\text{detection}}$= 4.2%, 95% CI: 1.9%, 9.5%), but also for standardized surveys where drivers are actively searching for roadkill. In consequence, through lack of detection alone, citizen science datasets may underestimate the mortality of bigger species by about 75%, while we could have previously assumed that ungulate carcasses —such as the roe deer— were most likely always detected by
drivers. In addition to a general under-estimation of the total number of carcasses, differences in detectability between species or roads will introduce biases in the datasets. Therefore, modelling raw citizen science roadkill data without correcting for differences in detectability will likely result in inaccurate conclusions, as the relative frequencies of reports for each species do not represent the true ranking of collision rates (see also Collinson et al., 2014), and this also applies to surveys conducted by professionals (Teixeira et al., 2013). Differences in roadkill detectability may also be influenced by other spatio-temporal factors, such as the presence of vegetation on roadsides, road intersections or any configuration that may require the full attention of the driver, weather, and, of course, the amount of daylight. Although Collinson et al. (2014) found no effect of the position of the sun (which was thought to blind drivers depending on hours of the day), there has been, to the author's best knowledge, no attention given to these possible predictors of roadkill detectability, in the context of citizen science or otherwise. Further investigation of these predictors is therefore needed for accurate spatio-temporal modelling of WVC.

Following the detection of roadkill, drivers may not always immediately able to report the observation
(usually *via* a mobile app) to the monitoring scheme, and, in some cases, they may forget the animal
altogether. This issue is recognized in citizen science biodiversity monitoring schemes and often put
under the umbrella of general issues with uneven monitoring pressure (Johnston et al., 2023). However,
as explained in chapter 4, failure to discriminate between the two processes may lead to the incorrect
inference of pseudo-absence data, which an approach classically employed for modelling opportunistic,
presence-only, data of a species' presence (Mair & Ruete, 2016). The lack of *willingness to report* had, to
the author's best knowledge, never been formally demonstrated with an experimental approach in citizen
science projects with opportunistic sampling. We found that only 33.5% of the detections (95% CI:
5.4,72.2) are effectively reported to the Faune-AuRA database. By combining the probabilities for
carcass detection and reporting, we can obtain the probability for an animal present on the road to be
reported to the Faune-AuRA database. For example, the probability for a roe deer carcass (~ 25kg) to be
reported to Faune-AuRA when a contributor drives by is P*detection* . P*report | detected* = 0.267. 0.335 = 0.089
(95% CI: 0.009, 0.29). Of course, we assume here that the probabilities are independent, *i.e.*, that the
detectability of the animal does not impact its odds of being reported and vice versa.
In addition to a general under-reporting of the roadkill carcasses, authors have raised the issue of preferential reporting in opportunistic monitoring, where some species have a higher probability of being reported than others as they seem to be over-represented in roadkill datasets (Swinnen et al., 2022). This could possibly be the case for rare, endangered or charismatic species for which reports of mortality may be perceived as more critical. Preferential reporting of rarer species has been noted by Johnston et al. (2023) in the biodiversity monitoring scheme eBird, and in roadkill reporting by Périquet et al. (2018). However, contributors may instead prefer to report more easily identifiable species (Tiago et al., 2017). Selective reporting in opportunistic monitoring would have significant impacts on the accuracy of the data, for example for use in species distribution modelling (Tiago et al., 2017), and there is therefore a need for experimental designs that can detect and quantify the presence of this bias.

Proposing an experimental design to assess selective reporting biases in roadkill monitoring

An experimental design for detecting selective reporting biases can be built upon the design presented in chapter 4. By exposing naive contributors driving on the road to pairs of artificially placed roadkill, we can test whether they choose to report only one the carcasses from each pair. Selected pairs of species can vary in their (1) charisma (e.g., Eurasian lynx), under the hypothesis that charismatic species are more often reported, or (2) abundance, under the hypothesis that the species contributors are most familiar with are also more frequently reported. If there is no prior information available on the detectability of each species, then effort should be made to standardize the detection probability of the pairs of species, through similar sizes and taxa (see manuscript n°4, Collinson et al., 2014; R. A. L. Santos et al., 2016; Teixeira, Coelho, Esperandio, & Kindel, 2013 for predictors of detectability). Of course, the level of charisma possessed by a species is an inherently subjective feature (Albert et al., 2018), and may need to first be assessed through surveys.
Roadkill dataset corrections in manuscript n° 2, 4 and in chapter 4 have made one important assumption regarding roadkill persistence: that all roads of the AuRA region were monitored daily by project contributors. In fact, it is not the persistence of roadkill alone, but the combination of persistence and of the presence of an observer that is relevant to roadkill monitoring. While in standardized monitoring the presence of observers is known with certainty (Teixeira et al., 2013), opportunistic citizen science frequently encounters challenges stemming from inconsistent and unbalanced sampling efforts, both in terms of location and time (Bíl, Heigl, et al., 2020; Geldmann et al., 2016). The resulting data is defined as “presence-only”, with no immediate way to distinguish the true absence of a species from the lack of presence of an observer (Johnston et al., 2023). Although presence-only data may be useful for informing population trends and modelling species distributions (Horns et al., 2018; Tiago et al., 2017), these irregular sampling patterns can introduce detected trends in the data that do not reflect actual ecological realities (Isaac et al., 2014), such as variations in observer’s efforts inducing temporal patterns in the number of record that are not related to changes in the species’ abundance (Knape et al., 2022), or spatial patterns in reports that mirror the distribution of the observers’ homes (Dennis & Thomas, 2000).

To address this issue, there is a limited number of methods that can be applied. For example, proxies of the sampling effort in citizen science biodiversity monitoring schemes can be used in order to correct the data (Cretois et al., 2021; Ver Hoef et al., 2021). Sampling effort in these projects has been shown to be reflected by the density of human populations and the accessibility of the sampling areas (Geldmann et al., 2016; Mair & Ruete, 2016; Sicacha-Parada et al., 2021). In addition, other observations made within the same citizen science project can be used: the length of the recorded list in list-based sampling (i.e., listing all species sighted during a recording session, e.g., ebird.org) can be introduced as a proxy of sampling effort (Horns et al., 2018), and, similarly, the reports submitted by an observer of species that are not the focal species in an area can be used to infer pseudo-absence points for said focal species (de Groot et al., 2022; Milanesi et al., 2020). In the case of opportunistic roadkill datasets, the potential biases introduced by inconsistencies in the sampling effort are often acknowledged, but seldom addressed (see Hiegl et al., 2016; Olson et al., 2014; Périquet et al., 2018; Shin, Kim, Groffen, Woo, Song, & Borzée, 2022; Yue et al., 2019). Some opportunistic roadkill monitoring schemes now offer the possibility to conduct standardized roadkill counts along transects, although it seems that even then, the overwhelming majority of reports remain opportunistic (Swinnen et al., 2022). Considering that most roadkill monitoring conducted by volunteers focuses on all roadkill, indiscriminately of the species, pseudo-absence points could be inferred for species of interest from the reports of other species on the
road (de Groot et al., 2022). Potential issues with this approach can arise if project contributors preferentially report certain species, which once again emphasizes the importance of detecting *selective reporting* biases in these datasets.

Proposing an experimental design for inferring pseudo-absence points in Faune-AuRA

Faune-AuRA is a unique example of a roadkill monitoring scheme that is part of a larger monitoring scheme, where contributors report the presence of all wild species. In consequence, the roadkill database is in fact a subsample of the reports contained within the Faune-AuRA project, for which the animals have been marked as “killed by a vehicle”. In this context, the sampling effort of contributors in reporting non-roadkill observations can be used to inform the monitoring pressure for roadkill. We show in figure 1 an example of the spatio-temporal distribution of the monitoring pressure in the Ain department, France. Figure 1a shows a map of the spatial distribution of the roadkill monitoring pressure, defined as the number of contributors that have reported at least one animal to Faune-AuRA in 2022 (excluding roadkill reports). Some areas of the department seem to have higher densities of active project contributors, and thus the frequency of road surveys in these hotspots is expected to much higher than in other areas. Figure 1b shows the temporal variations in the monitoring pressure with a similar approach. This type of data could lead to a spatially and temporally structured indicator of the monitoring pressure, describing the frequency of road surveys for roadkill in the AuRA region. This indicator of the monitoring pressure could then be integrated into the equation for roadkill reports correction presented in Chapter 4.

However, this type of approach needs further work to be reliable. For example, it is worth considering
that, at finer spatial scales, more frequented roads have a higher probability of being used by contributors
(Paul et al., 2014). In addition, contributor activity may sometimes misrepresent the monitoring pressure:
there were two notable peaks in contributor activity on January 29th and 30th, 2022 (fig. 1b). These dates
correspond to the national citizen science LPO reporting project “Oiseaux des jardins” (“Garden birds”),
a national bird counting scheme which may draw many contributors that are not otherwise active in
Faune-AuRA during the rest of the year. In general, it is worth considering that Faune-AuRA attracts
many types of volunteer contributors, some of which may only be active in one-off monitoring schemes,
and others that do not contribute to road mortality reports (as a more recent feature of the monitoring
scheme, the mortality module is not known to all users). Therefore, a preliminary filtering of the
contributors appears necessary to retain only users whose reporting activity for living specimens is likely
to reflect their monitoring of roadkill.
<figure><img src="image_69.png" /><figcaption>Fig. 1: **Spatial (a) and Temporal (b) distributions of active Faune-Ain contributors (2022) in the Ain department, AuRA region, France.** (a) in each 5km by 5km cell of the grid, we have computed the number of project contributors having submitted at least on report of a wildlife species (living specimens) to the Faune-Ain wildlife monitoring scheme; (b) the same type of approach has been applied to represent the number of active contributors (>1 contribution) for each day of the year.</figcaption></figure>
## 1.2. Beyond citizen science: the problem of roadkill persistence

The short persistence of roadkill is a source of under-estimation in roadkill datasets that is not limited to citizen science monitoring schemes, but is instead transversal to all methods of roadkill monitoring (Santos et al., 2011). Most studies published to this day find that carcasses are not likely to remain visible for more than a few days on roads (Antworth et al., 2005; Guinard et al., 2012; Henry et al., 2021; Santos et al., 2011), which means that roadkill monitoring needs to be conducted frequently in order to avoid missing a large portion of the road mortality. The estimates of roadkill persistence presented in chapter 2 are consistent with previously published estimates, and we have been able to fine-tune these estimates of persistence for a number of species. We found characteristic persistence estimates (i.e., time for 63% of the carcasses to be removed, Teixeira et al., 2013) ranging from less than 2 hours (small passerines, manuscript n°2) to 59 hours (medium to large-bodied birds and mammals, case study n°1). From these results, we can conclude that (1) roadkill available for detection to Faune-AuRA contributors only represents a small portion of the actual wildlife-vehicle collisions in the region, and (2) since removal rates differ between species, the most often reported species in the database are once again not necessarily the species most often killed on roads.

We considered road traffic volume, species' body mass, season, and rainfall as possible predictors for roadkill persistence in manuscripts n° 2 and 3, as well as in the case study presented in chapter 2. Although neither season nor rainfall modified the persistence of roadkill, we found that road traffic volume was a predictor of amphibian persistence; with longer persistence times on less frequented roads, but it had no impact on persistence for other species (manuscripts n°2 and 3). In contrast, the *citizen science* approach (case study n°1) revealed that medium to large avian and mammalian species' persistence was positively linked to traffic volume (consistent with Santos & Ascensão, 2019; Santos et al., 2011). Therefore, at first glance, we find no consistent answer to the existence and nature of the relation between road traffic volume and roadkill persistence.

However, some elements should be discussed regarding these results. The major difference between the experiment in case study n°1 (citizen science approach) and manuscript n°3 is that the former included observations on french highways, a type of road we could not access in manuscript n°3 for safety reasons. Highways in France are multi-lane roads, with fencing to prevent human and wildlife access to the road surface, and where only highway patrols are permitted to step out of a vehicle to, for example, remove roadkill carcasses. In this case, it seems that the persistence of roadkill was predicted by the type of road, more than it was by the volume of traffic itself, with carcasses persisting significantly longer on highways compared to the rest of the road network. Santos et al. (2019) have also found substantially
longer persistence for rodent roadkill on four-lane roads, compared to two-lane roads and dirt roads. Guinard et al. (2012) found median persistence times of about 10 days for small bird species on french highways, which is much higher than the results we obtained for these species on the rest of the road network (manuscripts n°2 and 3). In allowing us to expend the scale of our experiments to new types of roads at next to no financial costs, citizen sciences have showed considerable promise for the study of roadkill persistence. Finally, the differences in response to road traffic volume between amphibians and small passerines is less straightforward to explain, as both were tested on similar roads. Considering their size and low detectability, removal by members of the public is unlikely, and we found little evidence of scavenger activity for removing roadkill, making it plausible that some of these carcasses disappeared through repeated crushing by vehicles (see manuscript n°3). This result warrants a more in-depth analysis of the speed of disappearance of carcasses when repeatedly crushed, depending on their characteristics, such as morphology or bone density. Furthermore, we may have underestimated scavenging rates on small animals (see Antworth et al., 2005 for scavenging rates of small birds), as the experiments presented in manuscript n°3 showing little scavenger activity in the Ain department of AuRA focused on larger species.

Manuscript n°3 showed that road users play a significant part in the removal of roadkill in the Ain department, a phenomenon that may extend to other parts of the region according to case study n°1, where some carcass were found intact in ditches, suggesting displacement by a person. The cultural characteristics of the study area can possibly influence the prevalence of this habit, which is seldom considered as a potentially significant source of removal in current roadkill persistence studies (Santos et al., 2011). The extent of this phenomenon, which may lead to large carcasses being removed very quickly from roads, needs further attention, as is it relevant to both roadkill studies, but can also have significance for the way roadkill defines the relation between members of the public and wildlife (Lulka, 2008; Lunney, 2013). Therefore, roadkill persistence studies may have to draw insights from sociological studies, which may help explain and predict rapid roadkill removal by people.

Finally, the experiments presented in manuscript n°2 found that amphibian (~ 30g) and passerine (< 20g) carcasses disappeared between 2 and 100 times faster than the medium-to-large avian and mammalian species we used in manuscript n°3 and case study n°1. Although we conclude that there is no evidence of species' body mass being a predictor of roadkill persistence in medium-to-large species, it appears that this is not true for extreme values, such that very small species disappeared from roads significantly faster. Therefore, the generalization of the persistence estimates we have obtained throughout this project to other taxa (e.g., reptiles) and species with extreme values of body mass (e.g., bats, small rodents) is not straightforward. Although some authors have produced estimates of the median persistence times for
small rodents (Santos & Ascensão, 2019), and bats, lizards and snakes (Santos et al., 2011), we have also discussed throughout this manuscript that the context of each study seems to affect persistence estimates, and assessing the pertinence of these estimates for roadkill in the AuRA region may prove difficult. As an illustration, vipers *Crotalus oreganus* persisted about 48 hours in Winton et al. (2018), compared to 24 hours in other experiments (Santos et al., 2011). In conclusion, we find that, overall, roadkill persistence for most species remained largely unaffected by seasonal variations, rainfall patterns, or the volume of road traffic within the Auvergne-Rhône-Alpes region. This conclusion can be considered advantageous, as it greatly simplifies the dataset correction process by requiring a single correction parameter of roadkill persistence for all roads and times of year.

A note on the accuracy of reports from non-professional observers

The correction method presented in this manuscript (chapter 4) explicitly integrates the theoretical framework for roadkill reporting by volunteers following a wildlife-vehicle collision (i.e., the carcass is still present on the road when a project contributor drives by, it is detected and it is reported). In consequence, it assumes that the roadkill reports that are made are accurate. In all citizen science projects focused on ecological surveys, there is a possibility of inaccurate reports due for example to contributors mistaking one species for another (Van Eupen et al., 2022). When it comes to roadkill, the challenge of species misidentification is heightened by the difficulty in identifying flattened remains (Bíl, Heigl, et al., 2020). To minimize the probability of erroneous reports in citizen science data, several measures can be implemented: allowing users to provide their level of confidence in identifying the species (e.g., Kreling et al., 2019; Waetjen & Shilling, 2017); enabling community reviewing of the reports (note that multiple non-professional reviewers agreeing on the identification can provide high levels of confidence, Hsing et al., 2018); implementing reports reviewing by professionals (e.g., Shilling & Collinson, 2017); flagging implausible reports (e.g., Bonter & Cooper, 2012). For photographic data, automated verification methods with machine learning classifiers (e.g., Rigoudy et al., 2023; Willi et al., 2019) may prove helpful, although not yet applied to roadkill data. Faune-AuRA implements automatic reviewing by professionals of the implausible reports (i.e., rare species or reports outside of the expected spatio-temporal distribution of the species).

Another issue that can be considered in citizen science biodiversity monitoring is the level of spatio-
temporal precision of reports. However, many monitoring schemes nowadays utilize the GPS capabilities
of modern smartphones to accurately pinpoint the location of the animal (Vercayie & Herremans, 2015).
Typically, modern smartphones offer GPS horizontal accuracy within a range of 5 to 10 meters (Hess et al., 2012; Merry & Bettinger, 2019). Reports submitted through mobile apps also typically rely on the internal clock of the phone as the default timestamp. Although smartphones seem to be the current standard for roadkill reporting (Bíl, Heigl, et al., 2020), some monitoring projects rely on additional means of reporting data such as social media (e.g., *The Road Lab*, formerly *Project Splatter*, UK) may suffer from less accurate reports, which may interfere with the proper spatial and temporal modelling of the data.

## 1.3. Correcting opportunistic citizen science roadkill datasets and implications for wildlife conservation

In conclusion, how biased are datasets from opportunistic roadkill monitoring? Although issues regarding the monitoring pressure were beyond the scope of this project, we have successfully converted the number of reports into estimates of the wildlife-vehicle collisions in the Auvergne-Rhône-Alpes region for a select set of species. In order to achieve this, we rely on the assumption that roads are, on average, monitored daily by contributors, which may not be unreasonable when focusing on large spatial scales. We found little support for temporal structures in the correction parameters (persistence, detection, report) when we explored the effects of rain and season on roadkill persistence. In addition, we found little support for a spatial structure in these parameters, as road traffic volumes only affected the speed at which amphibian roadkill disappeared. Although there are several spatio-temporal predictors for these parameters that remain to be investigated, the information presently available to us leads to the conclusion that the spatial and temporal distributions of the roadkill reports in Faune-AuRA are representative of the true distribution of wildlife-vehicle collisions. However, we have left aside a central aspect of opportunistic monitoring schemes that may introduce significant spatial and temporal bias (Dennis & Thomas, 2000; Sauermann & Franzoni, 2015): the monitoring pressure. As a result, we are currently unable to produce spatial (map of wildlife-vehicle collision hotpots) or temporal (trends in WVC) distributions of roadkill.

Nevertheless, we find in chapter 4 that the reports in the database represented between 3.7% and 10.8% of the true extent of road mortality (respectively for the red squirrel *Sciurus vulgaris* and the red fox *Vulpes vulpes*, chapter 4, table 1). In addition, the 21 species of small passerines presented in manuscript n°2 were estimated to be reported only 2.8% of the time *before* we corrected for detection and reporting probabilities. In the light of the rest of the results presented here, we can estimate the detection rate of
small passerines at 4.2% (chapter 3) and their reporting rate at 33.5% (chapter 4), which results in a final estimate of 504 000 collisions with vehicles in 2022 at the scale of the Auvergne-Rhône-Alpes region. Of course, the pertinence of these estimates is considerably weighted down by the large uncertainty surrounding the reporting probabilities (95% CI: 5%, 72%), which means that they range from reasonable (236 000 passerines killed on roads in 2022) to catastrophic (3 377 600 individuals killed). Based on the author's own experience as a Faune-AuRA contributor, along with other testimonies, small species such as passerines can only be identified after turning around, stopping the car and assessing the carcass on foot, which suggest that the lower tail of the confidence interval (probability of report: 5%) may not be absurd. In consequence, small passerine mortality in the region may be higher than current estimates and tend toward the catastrophic estimates of several million collisions annually, which is a major cause for concern for these populations. In addition, these testimonies add to the hypothesis that contributors may report selectively, based on the effort required for identification—which may be expressed in relation to the species' size, coloring, or other unique features—such that individuals are more likely to be reported when they can be identified at first glance (see also Tiago et al., 2017).

There are limited examples of calculated roadkill rates available in the literature to assess the plausibility of our results. Canova & Balestrieri (2019) estimated the annual roadkill rate of the red fox at 0.06 collision.km⁻¹ in Italy, while we estimated 0.024 (95% CI: 0.004, 0.7) collisions.km⁻¹. However, annual common buzzard mortality, which we estimated at 0.006 collisions.km⁻¹ (95% CI: 0.009,0.24), is comparable to estimated roadkill rate for all avian species in southern China (Wang et al., 2022). Of course, the comparison of the estimates of difficult as they often represent raw roadkill counts that are not corrected for roadkill persistence or detectability, and, as highlighted by the first chapter of this manuscript, characteristics of the landscape, population density, time of year, or even population-level behaviours may result in significant variations in the patterns of space-use of the individuals, and therefore, in the collision risks. In general, it is difficult to assess the conservation implications of the road mortality estimates that we have produced, as they need to be placed within the larger context of population demography. For examples, population sizes for foxes, squirrels, buzzards and small passerines are not currently available in France, which limits the conclusions we can draw regarding the impact of road mortality on these species.

Nevertheless, Hill et al. (2019) estimated that wildlife mortality resulting from collisions on transport infrastructures represented less than 10% of the total mortality for most species (based on a meta-analysis of wildlife telemetry data), while legal harvest was the leading source of anthropogenic mortality. However, road mortality represents a source of mortality that is entirely accidental, whereas legal harvest or hunting are intentional and benefit the people involved. This means that road mortality can be reduced
with little repercussion for human populations, as next to no-one profits from roadkill (but see Abbate, 2019), and conservation efforts may turn toward road mortality as leverage for protecting endangered species. In fact, reducing wildlife mortality from vehicular collisions may be beneficial for societies in general, as collisions, particularly with large species, may pose significant risks for the safety of road users, and generate high costs when the vehicles are damaged (Seiler, 2005). In addition, several authors have expressed their concerns that the ubiquity of roadkill in everyday life may transform the relations between our societies and wildlife, specifically wildlife deaths (Lulka, 2008; Taylor, 2022), which may impact future conservation efforts.

## 2. Toward a predictive model for WVC

The initial objective of this thesis was to work toward a predictive model for wildlife-vehicle collisions. As explained in the introduction, predictive models can utilize observed patterns (statistical modelling) or focus on the underlying mechanics of the system under study (mechanistic modelling) to generate predictions (Denny & Benedetti-Cecchi, 2012). In both cases, reliable empirical data is essential for either constructing or validating the predictive model. Therefore, this project first focused on developing a mechanistic model of wildlife-vehicle collisions, which is an area that has received limited attention in the field of road ecology, and subsequently, efforts were made to obtain reliable data on wildlife-vehicle collisions through the improvement of an existing roadkill database.

### 2.1. Mechanistic approaches to WVC: what's around the bend?

The mechanistic model we presented can lead to several predictions for wildlife-vehicle collision (WVC) risks. Specifically, our model highlights the significance of animal movement and space use on WVC occurrences, which in turn helps identify animal behaviors, landscape configurations, and time windows that may correlate with higher road mortality in wildlife. In appendix C of the manuscript, we showed a strong association between the number of WVC reports in the Faune-AuRA database and the movement speed of male roe deer (*Capreolus capreolus*) obtained through GPS tracking, effectively validating one of the predictions made by the model. Our model is likely to produce three types of predictions: (i) certain species are more prone to being killed by vehicles due to specific behaviors they display (e.g.,
avian carrion-feeders that forage on roadkill are predicted to be at higher risks of collision compared to their terrestrial counterparts); and within a given population, (ii) there will be temporal variations in WVC occurrences that reflect changes in space-use and movement patterns, and (iii) there will be localized hotspots of collisions that reflect landscape configurations promoting animal mobility and contact with the road. While these broad categories of predictions may seem trivial when listed in that way, the strength of mechanistic modelling lies in providing precise predictions within each category.

However, testing these predictions will require either (i) comparing differences in WVC occurrences between species, (ii) analyzing temporal patterns in WVC, or (iii) investigating spatial patterns in WVC. As we previously mentioned, we have so far found little spatial and temporal structures in the correction parameters for the Faune-AuRA database, and suspect that a large part of the spatio-temporal biases are the result of the distributions of contributors, which are not currently integrated into the correction process. However, we have achieved reliable estimates for the overall number of collisions in the AuRA region for each species, and have applied this correction process in manuscripts n°2, 3 and 4 using small passerine species (< 20g), the red fox *Vulpes vulpes*, the common buzzard *Buteo buteo*, and the red squirrel *Sciurus vulgaris* as examples. In this context, we could consider correcting the Faune-AuRA database for select species to help validate or invalidate predictions of the mechanistic model. For instance, the diet of a species can provide insights into estimating the size of their home range, with carnivores occupying larger areas (Gittleman & Harvey, 1982). By comparing the collision rates across species with different diets, we could verify the model's prediction that species occupying larger home ranges may encounter higher risks of collisions. In fact, the link between diet and collision risks has been noted by other authors, although they found that carnivorous species were less often reported as killed by vehicles (Barthelmess, 2010; Cook & Blumstein, 2013; Ford & Fahrig, 2007), which means that, in this case, there is a discrepancy between the predictions of the model and empirical findings. This may result from some simplifying assumptions that we have made (such as the lack of avoidance behaviours from animals stepping on the road): failure to validate model predictions only leads to improving the accuracy of the model.

In general, the mechanistic model demonstrates that paradigms derived from movement ecology, such as the relations between home-range size and body mass (Makarieva et al., 2005), or diet (Gittleman & Harvey, 1982), the existence of territorial or mate-searching behaviours that may vary throughout the seasons (Potts & Lewis, 2014), the influence on animal trajectories of the distribution of resources (Pyke et al., 1977) and other characteristic of the landscape (e.g., fire-prone habitats, Nimmo et al., 2019 and human-modified landscapes, Fahrig, 2007), as well as the role of cognition in the movements of animals (Fagan et al., 2013) should be at the forefront of future WVC research, both empirical and theoretical.
For example, a promising avenue for the study of collision risks is the movement ecology concept of *boundary responses*, which are responses of animals to encountering a boundary between the patch they are currently moving in, and the neighbouring patch (Fahrig, 2007). Boundary responses may result in animals remaining in the current patch and not crossing to the next. This response can be predicted by the species and type of habitat patch under consideration, with for example forested patches eliciting often stronger motivation in insects to stepping over the boundaries and access the neighbouring patches (Fahrig, 2007). The concept of boundary response echoes findings from empirical WVC studies, where the type of habitat predicted the risks of collisions: the probability of animals stepping on the road may vary according to the type of habitat that borders the road (Kreling et al., 2019; Malo et al., 2004; Pagany, 2020).

## Perspectives for a more comprehensive mechanistic model of WVC

By definition, a model can never accurately represent the reality in all its complexity (Giere, 2004). In the case of the mechanistic model presented in chapter 1, we have approached the problem of wildlife-vehicle collisions through an agent-based model focusing on animal trajectories, which is reminiscent of the ideal gas model of molecule encounter rates (Hutchinson & Waser, 2007). However, if we have modelled both road encounter rate and collision rates, we have yet to address the second step of the wildlife-vehicle collision process as present in the conceptual model in the introduction of this manuscript (fig. 6). We described the collision probabilities for animals as resulting from three consecutive events: probability of encountering a road, probability of the animal making the decision to step on the road surface, and finally, probability of being hit while on the road. The mechanistic model has, so far, not addressed step 2: it has yet to include the behavioural reactions of an animal to encountering a road, save for a scenario of movement where we modelled a general avoidance of the road as a whole. In addition to the existing literature covering boundary responses in animal movement ecology, Jaeger et al. (2005) have presented a model for road-avoidance behaviours; noise avoidance, road surface avoidance, vehicle avoidance, all of which may prevent an animal from stepping onto the road surface.

The notable advantage of an agent-based simulatory approach to WVC modelling is that this type of model is both versatile and easily built-upon. For example, integrating decision-making processes into
the model to simulate road avoidance behaviours is relatively straightforward, such that the model may
produce road mortality predictions for species whose cognitive abilities may help avoid collisions (Lima
et al., 2015), or species that are easily deterred by the noise and disturbance produced by traffic (Forman
et al., 2002). This type of extension of the model is a promising avenue for exploring the validity of the
conceptual models, such as the model proposed by Seiler & Helldin (2006), where they postulate that
high volumes of traffic are associated with reduced risks of collisions as animals do not attempt to cross
these roads, a result that has been disputed by recent empirical studies (Grilo et al., 2015). In more
concrete terms, implementing into the model a road avoidance parameter contingent upon the volume of
traffic may help confirm the theoretical basis of this conceptual model, as well as identify which
parameter values (both road traffic and strength of road avoidance) can lead to the expected results. In
addition to animal behavioural reactions, the strength of this approach is that we may also build upon the
behaviours of drivers: authors have suggested that some drivers may intentionally target select species
when driving (Beckmann & Shine, 2012; Secco et al., 2014), but they may also swerve to intentionally
avoid animals present on the road (de Resende Assis et al., 2022).

## 2.2. Advances toward statistical modelling of WVC

The advances we have made toward more robust roadkill datasets provided by opportunistic reporting have major implications for the future of predictive modelling in WVC. Indeed, this type of monitoring is a cost-efficient way of procuring large datasets that cover large spatial and temporal scales, but also a large diversity of species. Opportunistic monitoring may also be of particular interest for modelling rare events (Robinson et al., 2018), such as species that are rarely found in professional roadkill surveys, by allowing significant increases in the sampling effort. As previously discussed, although there are examples of the use of uncorrected datasets for statistical inference of roadkill risks (Červinka et al., 2015; dos Santos et al., 2017; Kreling et al., 2019), concerns regarding the robustness and accuracy of these datasets may have hampered their use in WVC modelling, even though roadkill datasets from citizen science projects are available in a number of different countries.

The level of precision in roadkill data correction for the Faune-AuRA database is currently insufficient
for it to be a useful tool in checking mechanistic model predictions, or to be used in spatially or
temporally explicit statistical models of roadkill risks. However, we have made significant advances in
toward this goal, and believe that with continued effort, the challenges that remain can be overcome in
the near future. Beyond wildlife-vehicle collisions, our work provides insights on the accuracy of
opportunistic datasets of species' presence, which we have found to paint a very partial picture of the
reality in the case of road mortality. The methodologies we present should serve as a basis for extending
correction processes of this type of data to other areas of ecology and conservation, thereby improving
the accuracy of datasets that have tremendous potential for ecological research, but are currently not
exploited to their full potential (Chandler et al., 2017). Additionally, the mechanistic model we presented
is expected to mostly provide insights into the causal relations between animal movement and the
probability of roadkill incidents, providing future orientations for empirical studies of collisions. Indeed,
in ecology, there are often a wealth of potential predictors that can be explored for species distribution
modelling, and in this case, roadkill distribution modelling, and challenges may reside in selecting the
most promising predictors (Austin, 2002). In addition, we showed that many causal relations may not be
linear, which should help construct better statistical model for describing roadkill data, as linear
correlation are usually the default choice for many authors (Guinard et al., 2012).

**3. General conclusions**

We have addressed in this manuscript the issue of predictive modelling in wildlife-vehicle collisions through two complementary approaches: theoretical, or mechanistic modelling; and empirical, or statistical modelling. Although the current advancement of the correction process of the opportunistic roadkill dataset we have focused on is not sufficient to allow us to construct a statistical model of collision risks in the Auvergne-Rhône-Alpes region, we have made significant progress toward future application of statistical modelling techniques to opportunistic datasets, but also toward the exploitation of these datasets for hypothesis testing and model validation. In parallel, the mechanistic model of WVC we have presented yields predictions that are valuable to the issue of road mortality. This model allowed us to emphasize the importance of animal trajectories and therefore movement ecology in WVC research, and has for example helped predict that mitigation measures based on preventing animals from coming into contact with a road (crossing structures, fencing...) are more efficient than reducing the volume or speed of traffic.

Approaching more accurate estimates of the wildlife-vehicle collisions, especially at the scale of a region
of about 70 000km², helps situate this source of mortality within the context of global change as one of
the drivers of the current biodiversity decline. These estimates, and improvements in the accuracy of a
dataset as extensive as Faune-AuRA, are helpful tools for future studies focusing on the demography of
wild populations (Moore et al., 2023), or the evolutionary pressures generated by the presence of roads in
a landscape (Brady & Richardson, 2017). Wildlife mortality estimates may also lead to renewed efforts
in implementing mitigation measures, and to new perspectives for the identification of endangered
species. Beyond the issue of wildlife-vehicle collisions, mechanistic modelling is a promising tool for
conservationists (Finke et al., 2008; Mokany & Ferrier, 2011; Thompson et al., 2021), which helps
predict future response of populations to global change. Opportunistic monitoring on the other hand is
becoming increasingly popular, especially for informing species distributions (Chandler et al., 2017). By
examining the reporting process of one of these monitoring schemes, we promote their use for producing
scientific knowledge, but we also contribute to make these projects more popular with the general public
by demonstrating the usefulness work produced by volunteer contributors. Citizen sciences are also often
recognized as valuable education tools for the public (Kelemen-Finan et al., 2018), which may be
especially valuable in the light of the current biodiversity crisis.
REFERENCES – Introduction and General discussion

Abbate, C. (2019). Save the Meat for Cats: Why It's Wrong to Eat Roadkill. *Journal of Agricultural and Environmental Ethics*, 32(1), 165-182. https://doi.org/10.1007/s10806-019-09763-6

Aguilar, R., Cristóbal-Pérez, E. J., Balvino-Olvera, F. J., de Jesús Aguilar-Aguilar, M., Aguirre-Acosta, N., Ashworth, L., Lobo, J. A., Martén-Rodríguez, S., Fuchs, E. J., Sanchez-Montoya, G., Bernardello, G., & Quesada, M. (2019). Habitat fragmentation reduces plant progeny quality: A global synthesis. *Ecology Letters*, 22(7), 1163-1173. https://doi.org/10.1111/ele.13272

Albert, C., Luque, G. M., & Courchamp, F. (2018). The twenty most charismatic species. *PLoS ONE*, 13(7), e0199149. https://doi.org/10.1371/journal.pone.0199149

Al-Zahrani, M. S., Ragab, K., & Haque, A. U. (2011). Design of GPS-based system to avoid camel-Vehicle collisions: A review. *Asian Journal of Applied Sciences*, 4(4), 362-377. https://doi.org/10.3923/ajaps.2011.362.377

Ansong, M., & Pickering, C. (2013). Are Weeds Hitchhiking a Ride on Your Car? A Systematic Review of Seed Dispersal on Cars. *PLoS ONE*, 8(11), e80275. https://doi.org/10.1371/journal.pone.0080275

Antworth, R. L., Pike, D. A., & Stevens, E. E. (2005). Hit and run : Effects of scavenging on estimates of roadkilled vertebrates. *Southeastern Naturalist*, 4(4), 647-656. https://doi.org/10.1656/1528-7092(2005)004[0647:HAREOS]2.0.CO;2

Asari, Y. (2022). Decreased traffic volume during COVID-19 did not reduce roadkill on fenced highway network in Japan. *Landscape and Ecological Engineering*, 18(1), 121-124. https://doi.org/10.1007/s11355-021-00487-2

Ascensão, F., Clevenger, A., Santos-Reis, M., Urbano, P., & Jackson, N. (2013). Wildlife-vehicle collision mitigation: Is partial fencing the answer? An agent-based model approach. *Ecological Modelling*, 257, 36-43. https://doi.org/10.1016/j.ecolmodel.2013.02.026

Ascensão, F., Ribeiro, Y. G. G., Campos, Z., Yogui, D. R., & Desbiez, A. L. J. (2022). Forecasting seasonal peaks in roadkill patterns for improving road management. *Journal of Environmental Management*, 321, 115903. https://doi.org/10.1016/j.jenvman.2022.115903

Austin, M. P. (2002). Spatial prediction of species distribution: An interface between ecological theory and statistical modelling. *Ecological Modelling*, 157(2), 101-118. https://doi.org/10.1016/S0304-3800(02)00205-3

Baguley, T. (2004). Understanding statistical power in the context of applied research. *Applied Ergonomics*, 35(2), 73-80. https://doi.org/10.1016/j.apergo.2004.01.002

Baker, P. J., Dowding, C. V., Molony, S. E., White, P. C. L., & Harris, S. (2007). Activity patterns of urban red foxes (*Vulpes vulpes*) reduce the risk of traffic-induced mortality. *Behavioral Ecology*, 18(4), 716-724. https://doi.org/10.1093/beheco/arm035

Balčiauskas, L. (2012). The Influence of Roadkill on Protected Species and Other Wildlife in Lithuania. *2011 International Conference on Ecology and Transportation (ICOET 2011)*
Balčiauskas, L., & Jasiulionis, M. (2012). Reducing the Incidence of Mammals on Public Highways Using Chemical Repellent. *The Baltic Journal of Road and Bridge Engineering*, 7(2), Art. 2.
https://doi.org/10.3846/bjrbe.2012.13

Barrientos, R., Ascensão, F., D'Amico, M., Grilo, C., & Pereira, H. M. (2021). The lost road : Do transportation networks imperil wildlife population persistence? *Perspectives in Ecology and Conservation*, 19(4), 411-416. https://doi.org/10.1016/j.pecon.2021.07.004

Barrientos, R., & Bolonio, L. (2009). The presence of rabbits adjacent to roads increases polecat road mortality. *Biodiversity and Conservation*, 18(2), 405-418. https://doi.org/10.1007/s10531-008-9499-9

Barrientos, R., Martins, R. C., Ascensão, F., D'Amico, M., Moreira, F., & Borda-de-Água, L. (2018). A review of researcher efficiency and carcass persistence in infrastructure-driven mortality assessment studies. *Biological Conservation*, 222(November 2017), 146-153.
https://doi.org/10.1016/j.biocon.2018.04.014

Barthelmess, E. (2010). The influence of body-size and diet on road-kill trends in mammals. *Biodiversity and Conservation*. https://doi.org/10.1007/s10531-010-9791-3

Bashore, T. L., Tzilkowski, W. M., & Bellis, E. D. (1985). Analysis of Deer-Vehicle Collision Sites in Pennsylvania. *The Journal of Wildlife Management*, 49(3), 769-774. https://doi.org/10.2307/3801709

Batty, M., & Torrens, P. M. (2001). Modelling complexity : The limits to prediction. *Cybergeo: European Journal of Geography*. https://doi.org/10.4000/cybergeo.1035

Bearman-Brown, L. E., Baker, P. J., Scott, D., Uzal, A., Evans, L., & Yarnell, R. W. (2020). Over-Winter Survival and Nest Site Selection of the West-European Hedgehog (*Erinaceus europaeus*) in Arable Dominated Landscapes. *Animals*, 10(9), Art. 9. https://doi.org/10.3390/ani10091449

Beckmann, C., & Shine, R. (2012). Do drivers intentionally target wildlife on roads? *Austral Ecology*, 37(5), 629-632. https://doi.org/10.1111/j.1442-9993.2011.02329.x

Bencin, H. L., Prange, S., Rose, C., & Popescu, V. D. (2019). Roadkill and space use data predict vehicle-strike hotspots and mortality rates in a recovering bobcat (*Lynx rufus*) population. *Scientific Reports*, 9(1), Art. 1. https://doi.org/10.1038/s41598-019-50931-5

Benten, A., Hothorn, T., Vor, T., & Ammer, C. (2018). Wildlife warning reflectors do not mitigate wildlife-vehicle collisions on roads. *Accident Analysis & Prevention*, 120, 64-73.
https://doi.org/10.1016/j.aap.2018.08.003

Bíl, M., Heigl, F., Janoška, Z., Vercayie, D., & Perkins, S. E. (2020). Benefits and challenges of collaborating with volunteers : Examples from National Wildlife Roadkill Reporting Systems in Europe. *Journal for Nature Conservation*, 54 (December 2019), 125798.
https://doi.org/10.1016/j.jnc.2020.125798

Bíl, M., Kušta, T., Andrášik, R., Cícha, V., Brodská, H., Ježek, M., & Keken, Z. (2020). No clear effect of odour repellents on roe deer behaviour in the vicinity of roads. *Wildlife Biology*, 2020(4), wlb.00744.
https://doi.org/10.2981/wlb.00744
Bird, T. J., Bates, A. E., Lefcheck, J. S., Hill, N. A., Thomson, R. J., Edgar, G. J., Stuart-Smith, R. D., Wotherspoon, S., Krkosek, M., Stuart-Smith, J. F., Pecl, G. T., Barrett, N., & Frusher, S. (2014). Statistical solutions for error and bias in global citizen science datasets. *Biological Conservation*, 173, 144-154. https://doi.org/10.1016/j.biocon.2013.07.037

Boarman, W. I., & Sazaki, M. (2006). A highway's road-effect zone for desert tortoises (*Gopherus agassizii*). *Journal of Arid Environments*, 65(1), 94-101. https://doi.org/10.1016/j.jaridenv.2005.06.020

Bonney, R., Cooper, C. B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K. V., & Shirk, J. (2009). *Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy*. *BioScience*, 59(11), 977-984. https://doi.org/10.1525/bio.2009.59.11.9

Bonter, D. N., & Cooper, C. B. (2012). Data validation in citizen science: A case study from Project FeederWatch. *Frontiers in Ecology and the Environment*, 10(6), 305-307. https://doi.org/10.1890/110273

Box, G. E. P. (1979). Robustness in the Strategy of Scientific Model Building. In R. L. Launer & G. N. Wilkinson (Éds.), *Robustness in Statistics* (p. 201-236). Academic Press. https://doi.org/10.1016/B978-0-12-438150-6.50018-2

Brady, S. P., & Richardson, J. L. (2017). Road ecology: Shifting gears toward evolutionary perspectives. *Frontiers in Ecology and the Environment*, 15(2), 91-98. https://doi.org/10.1002/fee.1458

Breiman, L. (1963). The Poisson Tendency in Traffic Distribution. *The Annals of Mathematical Statistics*, 34(1), 308-311. https://doi.org/10.1214/aoms/1177704267

Brieger, F., Kämmerle, J.-L., Hagen, R., & Suchant, R. (2022). Behavioural reactions to oncoming vehicles as a crucial aspect of wildlife-vehicle collision risk in three common wildlife species. *Accident Analysis & Prevention*, 168, 106564. https://doi.org/10.1016/j.aap.2021.106564

Bright, P. W., Balmforth, Z., & Macpherson, J. L. (2015). The effect of changes in traffic flow on mammal road kill counts. *Applied Ecology and Environmental Research*, 13(1), 171-179. https://doi.org/10.15666/aeer/1301_171179

Brown, C. R., & Bomberger Brown, M. (2013). Where has all the road kill gone? *Current Biology*, 23(6), R233-R234. https://doi.org/10.1016/j.cub.2013.02.023

Brown, G. P., Phillips, B. L., Webb, J. K., & Shine, R. (2006). Toad on the road: Use of roads as dispersal corridors by cane toads (*Bufo marinus*) at an invasion front in tropical Australia. *Biological Conservation*, 133(1), 88-94. https://doi.org/10.1016/j.biocon.2006.05.020

Brzeziński, M., Eliava, G., & Żmihorski, M. (2012). Road mortality of pond-breeding amphibians during spring migrations in the Mazurian Lakeland, NE Poland. *European Journal of Wildlife Research*, 58(4), 685-693. https://doi.org/10.1007/s10344-012-0618-2

Burgstahler, K., Isidro, J., Van Vuren, D. H., Collins, A. C., Aliperti, J. R., & Wells, C. P. (2023). Daily roadkill monitoring and long-term population census reveal female-biased mortality for a small mammal along a wildland-urban interface. *Biological Conservation*, 277, 109863. https://doi.org/10.1016/j.biocon.2022.109863
Callaghan, C. T., Poore, A. G. B., Hofmann, M., Roberts, C. J., & Pereira, H. M. (2021). Large-bodied birds are over-represented in unstructured citizen science data. *Scientific Reports*, 11(1), Art. 1. https://doi.org/10.1038/s41598-021-98584-7

Canova, L., & Balestrieri, A. (2019). Long-term monitoring by roadkill counts of mammal populations living in intensively cultivated landscapes. *Biodiversity and Conservation*, 28(1), 97-113. https://doi.org/10.1007/s10531-018-1638-3

Carr, L. W., Fahrig, L., & Pope, S. E. (2002). Impacts of Landscape Transformation by Roads. In K. J. Gutzwiller (Éd.), *Applying Landscape Ecology in Biological Conservation* (p. 225-243). Springer. https://doi.org/10.1007/978-1-4613-0059-5_13

Cassimiro, I. M. F., Ribeiro, M. C., & Assis, J. C. (2023). How did the animal come to cross the road? Drawing insights on animal movement from existing roadkill data and expert knowledge. *Landscape Ecology*, 38(8), 2035-2051. https://doi.org/10.1007/s10980-023-01671-5

Červinka, J., Riegert, J., Grill, S., & Šálek, M. (2015). Large-scale evaluation of carnivore road mortality: The effect of landscape and local scale characteristics. *Mammal Research*, 60(3), 233-243. https://doi.org/10.1007/s13364-015-0226-0

Chandler, M., See, L., Copas, K., Bonde, A. M. Z., López, B. C., Danielsen, F., Legind, J. K., Masinde, S., Miller-Rushing, A. J., Newman, G., Rosemartin, A., & Turak, E. (2017). Contribution of citizen science towards international biodiversity monitoring. *Biological Conservation*, 213, 280-294. https://doi.org/10.1016/j.biocon.2016.09.004

Chyn, K., Lin, T.-E., Wilkinson, D. P., Tracy, J. L., Lawing, A. M., & Fitzgerald, L. A. (2021). Fine-scale roadkill risk models: Understanding the intersection of wildlife and roads. *Biodiversity and Conservation*, 30(1), 139-164. https://doi.org/10.1007/s10531-020-02083-6

Clarke, G. P., White, P. C. L., & Harris, S. (1998). Effects of roads on badger *Meles meles* populations in south-west England. *Biological Conservation*, 86(2), 117-124. https://doi.org/10.1016/S0006-3207(98)00018-4

Coleman, J. L., Ford, N. B., & Herriman, K. (2008). A Road Survey of Amphibians and Reptiles in a Bottomland Hardwood Forest. *Southeastern Naturalist*, 7(2), 339-348. https://doi.org/10.1656/1528-7092(2008)7[339:ARSOAA]2.0.CO;2

Colino-Rabanal, V. J., Langen, T. A., Peris, S. J., & Lizana, M. (2018). Ungulate: Vehicle collision rates are associated with the phase of the moon. *Biodiversity and Conservation*, 27(3), 681-694. https://doi.org/10.1007/s10531-017-1458-x

Collinson, W. J., Marneweck, C., & Davies-Mostert, H. T. (2019). Protecting the protected: Reducing wildlife roadkill in protected areas. *Animal Conservation*, 22(4), 396-403. https://doi.org/10.1111/acv.12481

Collinson, W. J., Parker, D. M., Bernard, R. T. F., Reilly, B. K., & Davies-Mostert, H. T. (2014). Wildlife road traffic accidents: A standardized protocol for counting flattened fauna. *Ecology and Evolution*, 4(15), 3060-3071. https://doi.org/10.1002/ece3.1097
Cook, T. C., & Blumstein, D. T. (2013). The omnivore's dilemma: Diet explains variation in vulnerability to vehicle collision mortality. *Biological Conservation*, 167, 310-315.
https://doi.org/10.1016/j.biocon.2013.08.016

Coulson, G., Bender, H., Coulson, G., & Bender, H. (2019). Roadkill mitigation is paved with good intentions: A critique of Fox et al. (2019). *Australian Mammalogy*, 42(1), 122-130.
https://doi.org/10.1071/AM19009

Couvet, D. (2002). Deleterious Effects of Restricted Gene Flow in Fragmented Populations. *Conservation Biology*, 16(2), 369-376. https://doi.org/10.1046/j.1523-1739.2002.99518.x

Couvet, D., Jiguet, F., Julliard, R., Levrel, H., & Teyssedre, A. (2008). Enhancing citizen contributions to biodiversity science and public policy. *Interdisciplinary Science Reviews*, 33(1), 95-103.
https://doi.org/10.1179/030801808X260031

Cretois, B., Simmonds, E. G., Linnell, J. D. C., van Moorter, B., Rolandsen, C. M., Solberg, E. J., Strand, O., Gundersen, V., Roer, O., & Rød, J. K. (2021). Identifying and correcting spatial bias in opportunistic citizen science data for wild ungulates in Norway. *Ecology and Evolution*, 11(21), 15191-15204.
https://doi.org/10.1002/ece3.8200

Crino, O. L., Johnson, E. E., Blickley, J. L., Patricelli, G. L., & Breuner, C. W. (2013). Effects of experimentally elevated traffic noise on nestling white-crowned sparrow stress physiology, immune function and life history. *Journal of Experimental Biology*, 216(11), 2055-2062.
https://doi.org/10.1242/jeb.081109

Cukor, J., Havránek, F., Linda, R., Bukovjan, K., Painter, M. S., & Hart, V. (2018). First findings of brown hare (*Lepus europaeus*) reintroduction in relation to seasonal impact. *PLoS ONE*, 13(10), e0205078. https://doi.org/10.1371/journal.pone.0205078

D'Amico, M., Román, J., de los Reyes, L., & Revilla, E. (2015). Vertebrate road-kill patterns in Mediterranean habitats: Who, when and where. *Biological Conservation*, 191, 234-242.
https://doi.org/10.1016/j.biocon.2015.06.010

de Groot, M., Ogris, N., van der Meij, M., & Pocock, M. J. O. (2022). Where to search: The use of opportunistic data for the detection of an invasive forest pest. *Biological Invasions*, 24(11), 3523-3537.
https://doi.org/10.1007/s10530-022-02857-9

Deguines, N., Julliard, R., Flores, M. de, & Fontaine, C. (2012). The Whereabouts of Flower Visitors: Contrasting Land-Use Preferences Revealed by a Country-Wide Survey Based on Citizen Science. *PLoS ONE*, 7(9), e45822. https://doi.org/10.1371/journal.pone.0045822

Deletic, A., & Orr, D. W. (2005). Pollution Buildup on Road Surfaces. *Journal of Environmental Engineering*, 131(1), 49-59. https://doi.org/10.1061/(ASCE)0733-9372(2005)131:1(49)

Denneboom, D., Bar-Massada, A., & Shwartz, A. (2021). Factors affecting usage of crossing structures by wildlife – A systematic review and meta-analysis. *Science of The Total Environment*, 777, 146061.
https://doi.org/10.1016/j.scitotenv.2021.146061
Dennis, R. L. H., & Thomas, C. D. (2000). Bias in butterfly distribution maps : The influence of hot spots and recorder's home range. *Journal of Insect Conservation*, 4(2), 73-77.
https://doi.org/10.1023/A:1009690919835

Denny, M., & Benedetti-Cecchi, L. (2012). Scaling Up in Ecology : Mechanistic Approaches. *Annual Review of Ecology, Evolution, and Systematics*, 43(1), 1-22. https://doi.org/10.1146/annurev-ecolsys-102710-145103

de Resende Assis, J., Carvalho-Roel, C. F., Iannini-Custódio, A. E., Pereira, W. G., & Veloso, A. C. (2022). Snakes roadkill on highways in the Cerrado biome: An intentional conduct? *Studies on Neotropical Fauna and Environment*, 57(3), 198-205. https://doi.org/10.1080/01650521.2020.1844942

DeVault, T. L., Blackwell, B. F., Seamans, T. W., Lima, S. L., & Fernández-Juricic, E. (2014). Effects of Vehicle Speed on Flight Initiation by Turkey Vultures: Implications for Bird-Vehicle Collisions. *PLoS ONE*, 9(2), e87944. https://doi.org/10.1371/journal.pone.0087944

DeVivo, M. T., Cottrell, W. O., DeBerti, J. M., Duchamp, J. E., Heffernan, L. M., Kougher, J. D., & Larkin, J. L. (2011). Survival and cause-specific mortality of elk *Cervus canadensis* calves in a predator rich environment. *Wildlife Biology*, 17(2), 156-165. https://doi.org/10.2981/10-080

Dickinson, J. (2011, february 20). Citizen Science at the Cornell Lab of Ornithology. *American Association for the Advancement of Science* 2011 Annual Meeting.

Dickinson, J. L., Zuckerberg, B., & Bonter, D. N. (2010). Citizen Science as an Ecological Research Tool: Challenges and Benefits. *Annual Review of Ecology, Evolution, and Systematics*, 41(1), 149-172.
https://doi.org/10.1146/annurev-ecolsys-102209-144636

Donnelly, A., Crowe, O., Regan, E., Begley, S., & Caffarra, A. (2014). The role of citizen science in monitoring biodiversity in Ireland. *International Journal of Biometeorology*, 58(6), 1237-1249.
https://doi.org/10.1007/s00484-013-0717-0

dos Santos, S. R., Davis Jr., C. A., & Smarzaro, R. (2017). Analyzing Traffic Accidents based on the Integration of Official and Crowdsourced Data. *Journal of Information and Data Management*, 8(1), 67-82.

Downs, J. A., & Horner, M. W. (2012). Enhancing Habitat Connectivity in Fragmented Landscapes: Spatial Modeling of Wildlife Crossing Structures in Transportation Networks. *Annals of the Association of American Geographers*, 102(1), 17-34. https://doi.org/10.1080/00045608.2011.600190

Driessen, M. M. (2021). COVID-19 restrictions provide a brief respite from the wildlife roadkill toll. *Biological Conservation*, 256, 109012. https://doi.org/10.1016/j.biocon.2021.109012

Dugan, H. A., & Arnott, S. E. (2023). The ecosystem implications of road salt as a pollutant of freshwaters. *WIREs Water*, 10(2), e1629. https://doi.org/10.1002/wat2.1629

EEA greenhouse gases — data viewer. (2023, May 08). Retrieved from https://www.eea.europa.eu/data-and-maps/data/data-viewers/greenhouse-gases-viewer
Eigenbrod, F., Hecnar, S., & Fahrig, L. (2009). Quantifying the Road-Effect Zone: Threshold Effects of a Motorway on Anuran Populations in Ontario, Canada. *Ecology and Society*, 14(1).
https://doi.org/10.5751/ES-02691-140124

Elliott, K. C., & Rosenberg, J. (2019). Philosophical foundations for citizen science. *Citizen Science: Theory and Practice*, 4(1).

Ellis, M. M. (1936). Erosion Silt as a Factor in Aquatic Environments. *Ecology*, 17(1), 29-42.
https://doi.org/10.2307/1932951

Fagan, W. F., & Calabrese, J. M. (2014). The Correlated Random Walk and the Rise of Movement Ecology. *Bulletin of the Ecological Society of America*, 95(3), 204-206.

Fagan, W. F., Lewis, M. A., Auger-Méthé, M., Avgar, T., Benhamou, S., Breed, G., LaDage, L., Schlägel, U. E., Tang, W., Papastamatiou, Y. P., Forester, J., & Mueller, T. (2013). Spatial memory and animal movement. *Ecology Letters*, 16(10), 1316-1329. https://doi.org/10.1111/ele.12165

Fahrig, L. (2007). Non-optimal animal movement in human-altered landscapes. *Functional Ecology*, 21(6), 1003-1015. https://doi.org/10.1111/j.1365-2435.2007.01326.x

Farmer, R. G., & Brooks, R. J. (2012). Integrated risk factors for vertebrate roadkill in southern Ontario: Vertebrate Roadkill Risk Factors. *The Journal of Wildlife Management*, 76(6), 1215-1224.
https://doi.org/10.1002/jwmg.358

Fey, K., Hämäläinen, S., & Selonen, V. (2016). Roads are no barrier for dispersing red squirrels in an urban environment. *Behavioral Ecology*, 27(3), 741-747. https://doi.org/10.1093/beheco/arv215

Finke, J., Strein, M., & Sonnenschein, M. (2008). A simulation framework for modeling anthropogenic disturbances in habitat networks. *Ecological Informatics*, 3(1), 26-34.
https://doi.org/10.1016/j.ecoinf.2007.04.006

Ford, A. T., & Fahrig, L. (2007). Diet and body size of North American mammal road mortalities. *Transportation Research Part D: Transport and Environment*, 12(7), 498-505.
https://doi.org/10.1016/j.trd.2007.07.002

Forman, R. T. T., & Alexander, L. E. (1998). Roads and their major ecological effects. *Annual Review of Ecology and Systematics*, 29(1998), 207-231. https://doi.org/10.1146/annurev.ecolsys.29.1.207

Forman, R. T. T., Reineking, B., & Hersperger, A. M. (2002). Road traffic and nearby grassland bird patterns in a suburbanizing landscape. *Environmental Management*, 29(6), 782-800.
https://doi.org/10.1007/s00267-001-0065-4

Freedman, D. A. (2009). *Statistical Models: Theory and Practice*. Cambridge University Press.

Fu, B., Newham, L. T. H., & Ramos-Scharrón, C. E. (2010). A review of surface erosion and sediment delivery models for unsealed roads. *Environmental Modelling & Software*, 25(1), 1-14.
https://doi.org/10.1016/j.envsoft.2009.07.013
Garcia-Gonzalez, C., Campo, D., Pola, I. G., & Garcia-Vazquez, E. (2012). Rural road networks as barriers to gene flow for amphibians: Species-dependent mitigation by traffic calming. *Landscape and Urban Planning*, 104(2), 171-180. https://doi.org/10.1016/j.landurbplan.2011.10.012

Geldmann, J., Heilmann-Clausen, J., Holm, T. E., Levinsky, I., Markussen, B., Olsen, K., Rahbek, C., & Tøttrup, A. P. (2016). What determines spatial bias in citizen science? Exploring four recording schemes with different proficiency requirements. *Diversity and Distributions*, 22(11), 1139-1149.
https://doi.org/10.1111/ddi.12477

George, L., Macpherson, J. L., Balmforth, Z., & Bright, P. W. (2011). Using the dead to monitor the living: Can road kill counts detect trends in mammal abundance? *Applied Ecology and Environmental Research*, 9(1), 27-41.

Gerow, K., Kline, N., Swann, D., & Pokorny, M. (2010). Estimating annual vertebrate mortality on roads at Saguaro National Park, Arizona. *Human-Wildlife Interactions*, 4(2), 283-292.

Giere, R. N. (2004). How Models Are Used to Represent Reality. *Philosophy of Science*, 71(5), 742-752.
https://doi.org/10.1086/425063

Gippet, J., Rocabert, C., Fenet, S., Dumet, A., & Kaufmann, B. (2015). Modeling and evaluating human-mediated dispersal mechanisms at landscape scale: A study of road network and invasion parameters for Lasius neglectus ants invasive species. *World Conference on Natural Resource Modeling, Bordeaux, France*.

Girardet, X., Conruyt-Rogeon, G., & Foltête, J.-C. (2015). Does regional landscape connectivity influence the location of roe deer roadkill hotspots? *European Journal of Wildlife Research*, 61(5), 731-742. https://doi.org/10.1007/s10344-015-0950-4

Gittleman, J. L., & Harvey, P. H. (1982). Carnivore home-range size, metabolic needs and ecology. *Behavioral Ecology and Sociobiology*, 10(1), 57-63. https://doi.org/10.1007/BF00296396

Gladfelter, L. (1984). *Effect of wildlife highway warning reflectors on deer-vehicle accidents. (HR-210).*
*Iowa. Highway Division*. https://rosap.ntl.bts.gov/view/dot/23945

Glista, D. J. (2008). Vertebrate Road Mortality Predominantly Impacts Amphibians. *Herpetological Conservation and Biology*.

Glista, D. J., DeVault, T. L., & DeWoody, J. A. (2009). A review of mitigation measures for reducing wildlife mortality on roadways. *Landscape and Urban Planning*, 91(1), 1-7.
https://doi.org/10.1016/j.landurbplan.2008.11.001

González-Suárez, M., Ferreira, F. Z., & Grilo, C. (2018). Spatial and species-level predictions of road mortality risk using trait data. *Global Ecology and Biogeography*, 27(9), Art. 9.
https://doi.org/10.1111/geb.12769

Grilo, C., Ascensão, F., Santos-Reis, M., & Bissonette, J. A. (2011). Do well-connected landscapes promote road-related mortality? *European Journal of Wildlife Research*, 57(4), 707-716.
https://doi.org/10.1007/s10344-010-0478-6
Grilo, C., Bissonette, J. A., & Cramer, P. C. (2010). Mitigation measures to reduce impacts on biodiversity. In *Highways: Construction, Management, and Maintenance*.

Grilo, C., Borda-de-Água, L., Beja, P., Goolsby, E., Soanes, K., le Roux, A., Koroleva, E., Ferreira, F. Z., Gagné, S. A., Wang, Y., & González-Suárez, M. (2021). Conservation threats from roadkill in the global road network. *Global Ecology and Biogeography*, 30(11), 2200-2210. https://doi.org/10.1111/geb.13375

Grilo, C., Ferreira, F. Z., & Revilla, E. (2015). No evidence of a threshold in traffic volume affecting road-kill mortality at a large spatio-temporal scale. *Environmental Impact Assessment Review*, 55, 54-58. https://doi.org/10.1016/j.eiar.2015.07.003

Grilo, C., Koroleva, E., Andrášik, R., Bíl, M., & González-Suárez, M. (2020). Roadkill risk and population vulnerability in European birds and mammals. *Frontiers in Ecology and the Environment*, 18(6), 323-328. https://doi.org/10.1002/fee.2216

Grosman, P. D., Jaeger, J. A. G., Biron, P. M., Dussault, C., & Ouellet, J. P. (2009). Reducing moose-vehicle collisions through salt pool removal and displacement: An agent-based modeling approach. *Ecology and Society*, 14(2). https://doi.org/10.5751/es-02941-140217

Guinard, E., Billon, L., Bretaud, J.-F., Chevallier, L., Sordello, R., & Witté, I. (2023). Comparing the effectiveness of two roadkill survey methods on roads. *Transportation Research Part D: Transport and Environment*, 121, 103829. https://doi.org/10.1016/j.trd.2023.103829

Guinard, E., Julliard, R., & Barbraud, C. (2012). Motorways and bird traffic casualties: Carcasses surveys and scavenging bias. *Biological Conservation*, 147(1), 40-51. https://doi.org/10.1016/j.biocon.2012.01.019

Gunson, K. E., Mountrakis, G., & Quackenbush, L. J. (2011). Spatial wildlife-vehicle collision models: A review of current work and its application to transportation mitigation projects. *Journal of Environmental Management*, 92(4), 1074-1082. https://doi.org/10.1016/j.jenvman.2010.11.027

Gupta, S., Chand, D., & Kavati, I. (2021). Computer Vision based Animal Collision Avoidance Framework for Autonomous Vehicles. In S. K. Singh, P. Roy, B. Raman, & P. Nagabhushan (Éds.), *Computer Vision and Image Processing* (p. 237-248). Springer.

Gurrutxaga, M., Lozano, P. J., & Del Barrio, G. (2010). Assessing Highway Permeability for the Restoration of Landscape Connectivity between Protected Areas in the Basque Country, Northern Spain. *Landscape Research*, 35(5), 529-550. https://doi.org/10.1080/01426397.2010.504915

Hall, M. J., Martin, J. M., Burns, A. L., & Hochuli, D. F. (2021). Ecological insights into a charismatic bird using different citizen science approaches. *Austral Ecology*, 46(8), 1255-1265. https://doi.org/10.1111/aec.13062

Helldin, J. O. (2022). Are several small wildlife crossing structures better than a single large? Arguments from the perspective of large wildlife conservation. *Nature Conservation*, 47, 197-213. https://doi.org/10.3897/natureconservation.47.67979
Helldin, J. O., & Petrovan, S. O. (2019). Effectiveness of small road tunnels and fences in reducing amphibian roadkill and barrier effects at retrofitted roads in Sweden. *PeerJ*, 7, e7518.
https://doi.org/10.7717/peerj.7518

Hels, T., & Buchwald, E. (2001). The effect of road kills on amphibian populations. *Biological Conservation*, 99(3), 331-340. https://doi.org/10.1016/S0006-3207(00)00215-9

Henckel, L., Bradter, U., Jönsson, M., Isaac, N. J. B., & Snäll, T. (2020). Assessing the usefulness of citizen science data for habitat suitability modelling : Opportunistic reporting versus sampling based on a systematic protocol. *Diversity and Distributions*, 26(10), 1276-1290. https://doi.org/10.1111/ddi.13128

Henry, D. A. W., Collinson-Jonker, W. J., Davies-Mostert, H. T., Nicholson, S. K., Roxburgh, L., & Parker, D. M. (2021). Optimising the cost of roadkill surveys based on an analysis of carcass persistence. *Journal of Environmental Management*, 291, 112664. https://doi.org/10.1016/j.jenvman.2021.112664

Hess, B., Farahani, A. Z., Tschirschnitz, F., & von Reischach, F. (2012). Evaluation of fine-granular GPS tracking on smartphones. *Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems*, 33-40. https://doi.org/10.1145/2442810.2442817

Heigl, F., Stretz, C. R., Steiner, W., Suppan, F., Bauer, T., Laaha, G., & Zaller, J. G. (2016). Comparing road-kill datasets from hunters and citizen scientists in a landscape context. *Remote Sensing*, 8(10), 832.

Hill, J. E., DeVault, T. L., & Belant, J. L. (2019). Cause-specific mortality of the world's terrestrial vertebrates. *Global Ecology and Biogeography*, 28(5), 680-689. https://doi.org/10.1111/geb.12881

Hobday, A. J., & Minstrell, M. L. (2008). Distribution and abundance of roadkill on Tasmanian highways : Human management options. *Wildlife Research*, 35(7), 712-726.
https://doi.org/10.1071/WR08067

Hoermann, C. von, Pagany, R., Kirchner, K., Dorner, W., Heurich, M., & Storch, I. (2020). Predicting the Risk of Deer-vehicle Collisions by Inferring Rules Learnt from Deer Experience and Movement Patterns in the Vicinity of Roads. *2020 10th International Conference on Advanced Computer Information Technologies (ACIT)*, 368-373. https://doi.org/10.1109/ACIT49673.2020.9208843

Horns, J. J., Adler, F. R., & Şekercioğlu, Ç. H. (2018). Using opportunistic citizen science data to estimate avian population trends. *Biological Conservation*, 221, 151-159.
https://doi.org/10.1016/j.biocon.2018.02.027

Hsing, P.-Y., Bradley, S., Kent, V. T., Hill, R. A., Smith, G. C., Whittingham, M. J., Cokill, J., Crawley, D., Volunteers, M., & Stephens, P. A. (2018). Economical crowdsourcing for camera trap image classification. *Remote Sensing in Ecology and Conservation*, 4(4), 361-374.
https://doi.org/10.1002/rse2.84

Hsu, C.-H., & Lin, T.-E. (2021). Exploring the participation motivations of ongoing and former citizen scientists in Taiwan Roadkill Observation Network. *Journal for Nature Conservation*, 64, 126055.
https://doi.org/10.1016/j.jnc.2021.126055
Huijser, M. P., Fairbank, E. R., Camel-Means, W., Graham, J., Watson, V., Basting, P., & Becker, D. (2016). Effectiveness of short sections of wildlife fencing and crossing structures along highways in reducing wildlife-vehicle collisions and providing safe crossing opportunities for large mammals. *Biological Conservation*, 197, 61-68. https://doi.org/10.1016/j.biocon.2016.02.002

Hutchinson, J. M. C., & Waser, P. M. (2007). Use, misuse and extensions of « ideal gas » models of animal encounter. *Biological Reviews*, 82(3), 335-359. https://doi.org/10.1111/j.1469-185X.2007.00014.x

Inbar, M., Shanas, U., & Izhaki, I. (2002). Characterization of road accidents in israel involving large mammals. *Israel Journal of Ecology and Evolution*, 48(3), 197-206. https://doi.org/10.1560/XGJB-VR67-RFEQ-N27A

Isaac, N. J. B., Strien, A. J., August, T. A., Zeeuw, M. P., & Roy, D. B. (2014). Statistics for citizen science: Extracting signals of change from noisy ecological data. *Methods in Ecology and Evolution*, 5(10), 1052-1060. https://doi.org/10.1111/2041-210X.12254

Jaarsma, C. F. (1997). Approaches for the planning of rural road networks according to sustainable land use planning. *Landscape and Urban Planning*, 39(1), 47-54. https://doi.org/10.1016/S0169-2046(97)00067-4

Jaarsma, C. F., van Langevelde, F., & Botma, H. (2006). Flattened fauna and mitigation: Traffic victims related to road, traffic, vehicle, and species characteristics. *Transportation Research Part D: Transport and Environment*, 11(4), 264-276. https://doi.org/10.1016/j.trd.2006.05.001

Jacobson, S. L., Bliss-Ketchum, L. L., De Rivera, C. E., & Smith, W. P. (2016). A behavior-based framework for assessing barrier effects to wildlife from vehicle traffic volume. *Ecosphere*, 7(4), 1-15. https://doi.org/10.1002/ecs2.1345

Jaeger, J. A. G. G., Bowman, J., Brennan, J., Fahrig, L., Bert, D., Bouchard, J., Charbonneau, N., Frank, K., Gruber, B., & Von Toschanowitz, K. T. (2005). Predicting when animal populations are at risk from roads: An interactive model of road avoidance behavior. *Ecological Modelling*, 185(2-4), 329-348. https://doi.org/10.1016/j.ecolmodel.2004.12.015

Johnston, A., Matechou, E., & Dennis, E. B. (2023). Outstanding challenges and future directions for biodiversity monitoring using citizen science data. *Methods in Ecology and Evolution*, 14(1), 103-116. https://doi.org/10.1111/2041-210X.13834

Jones, D. N., Griffiths, M. R., Griffiths, J. R., Hacker, J. L. F., & Hacker, J. B. (2014). Implications of upgrading a minor forest road on traffic and road-kill in southeast Queensland. *Australasian Journal of Environmental Management*, 21(4), 429-440. https://doi.org/10.1080/14486563.2014.944590

Kang, W., Minor, E. S., Woo, D., Lee, D., & Park, C.-R. (2016). Forest mammal roadkills as related to habitat connectivity in protected areas. *Biodiversity and Conservation*, 25(13), 2673-2686. https://doi.org/10.1007/s10531-016-1194-7

Kelemen-Finan, J., Scheuch, M., & Winter, S. (2018). Contributions from citizen science to science education: An examination of a biodiversity citizen science project with schools in Central Europe.
*International Journal of Science Education*, 40(17), 2078-2098.

https://doi.org/10.1080/09500693.2018.1520405

Keller, I., & Largiadèr, C. R. (2003). Recent habitat fragmentation caused by major roads leads to reduction of gene flow and loss of genetic variability in ground beetles. *Proceedings of the Royal Society of London. Series B: Biological Sciences*, 270(1513), 417-423. https://doi.org/10.1098/rspb.2002.2247

Klemann-Junior, L., Vallejos, M. A. V., Scherer-Neto, P., & Vitule, J. R. S. (2017). Traditional scientific data vs. uncoordinated citizen science effort: A review of the current status and comparison of data on avifauna in Southern Brazil. *PLoS ONE*, 12(12), e0188819. https://doi.org/10.1371/journal.pone.0188819

Knape, J., Coulson, S. J., van der Wal, R., & Arlt, D. (2022). Temporal trends in opportunistic citizen science reports across multiple taxa. *Ambio*, 51(1), 183-198. https://doi.org/10.1007/s13280-021-01550-w

Koch, W., Hogeweg, L., Nilsen, E. B., & Finstad, A. G. (2022). Maximizing citizen scientists' contribution to automated species recognition. *Scientific Reports*, 12(1), Art. 1.

https://doi.org/10.1038/s41598-022-11257-x

Kreling, S. E. S., Gaynor, K. M., & Coon, C. A. C. (2019). Roadkill distribution at the wildland-urban interface. *Journal of Wildlife Management*, 83(6), 1427-1436. https://doi.org/10.1002/jwmg.21692

Kroeger, S. B., Hanslin, H. M., Lennartsson, T., D'Amico, M., Kollmann, J., Fischer, C., Albertsen, E., & Speed, J. D. M. (2022). Impacts of roads on bird species richness: A meta-analysis considering road types, habitats and feeding guilds. *Science of The Total Environment*, 812, 151478.

https://doi.org/10.1016/j.scitotenv.2021.151478

Kuipers, K. J. J., May, R. F., Graae, B. J., & Verones, F. (2019). Reviewing the potential for including habitat fragmentation to improve life cycle impact assessments for land use impacts on biodiversity. *The International Journal of Life Cycle Assessment*, 24(12), 2206-2219. https://doi.org/10.1007/s11367-019-01647-1

Lafortezza, R., & Chen, J. (2016). The provision of ecosystem services in response to global change: Evidences and applications. *Environmental Research*, 147, 576-579.

https://doi.org/10.1016/j.envres.2016.02.018

Larson, E. R., Graham, B. M., Achury, R., Coon, J. J., Daniels, M. K., Gambrell, D. K., Jonasen, K. L., King, G. D., LaRacuente, N., Perrin-Stowe, T. I., Reed, E. M., Rice, C. J., Ruzi, S. A., Thairu, M. W., Wilson, J. C., & Suarez, A. V. (2020). From eDNA to citizen science: Emerging tools for the early detection of invasive species. *Frontiers in Ecology and the Environment*, 18(4), 194-202.

https://doi.org/10.1002/fee.2162

Legagneux, P., & Ducatez, S. (2013). European birds adjust their flight initiation distance to road speed limits. *Biology Letters*, 9(5), 20130417. https://doi.org/10.1098/rsbl.2013.0417

Lehrer, E. W., Schooley, R. L., & Whittington, J. K. (2012). Survival and antipredator behavior of woodchucks (*Marmota monax*) along an urban–agricultural gradient. *Canadian Journal of Zoology*, 90(1), 12-21. https://doi.org/10.1139/z11-107
Lesbarrères, D., & Fahrig, L. (2012). Measures to reduce population fragmentation by roads : What has worked and how do we know? *Trends in Ecology & Evolution*, 27(7), 374-380.
https://doi.org/10.1016/j.tree.2012.01.015

Lesbarrères, D., Pagano, A., & Lodé, T. (2003). Inbreeding and road effect zone in a *Ranidae*: The case of Agile frog, *Rana dalmatina Bonaparte*, 1840. *Comptes Rendus Biologies*, 326, 68-72.
https://doi.org/10.1016/S1631-0691(03)00040-4

Lima, S. L., Blackwell, B. F., DeVault, T. L., & Fernández-Juricic, E. (2015). Animal reactions to oncoming vehicles: A conceptual review. *Biological reviews of the Cambridge Philosophical Society*, 90(1), 60-76. https://doi.org/10.1111/brv.12093

Litvaitis, J. A., & Tash, J. P. (2008). An approach toward understanding wildlife-vehicle collisions. *Environmental Management*, 42(4), 688-697. https://doi.org/10.1007/s00267-008-9108-4

Loraamm, R. W., & Downs, J. A. (2016). A wildlife movement approach to optimally locate wildlife crossing structures. *International Journal of Geographical Information Science*, 30(1), 74-88.
https://doi.org/10.1080/13658816.2015.1083995

Loss, S. R., Will, T., & Marra, P. P. (2014). Estimation of bird-vehicle collision mortality on U.S. roads. *The Journal of Wildlife Management*, 78(5), 763-771. https://doi.org/10.1002/jwmg.721

Louvrier, J., Papaïx, J., Duchamp, C., & Gimenez, O. (2020). A mechanistic–statistical species distribution model to explain and forecast wolf (*Canis lupus*) colonization in South-Eastern France. *Spatial Statistics*, 36, 100428. https://doi.org/10.1016/j.spasta.2020.100428

LPO France (2021). *Séminaire: Bases de données citoyennes et biodiversité LPO*. January 2021.

Lulka, D. (2008). The intimate hybridity of roadkill: A Beckettian view of dismay and persistence. *Emotion, Space and Society*, 1(1), 38-47. https://doi.org/10.1016/j.emospa.2008.08.008

Lunney, D. (2013). Wildlife roadkill: Illuminating and overcoming a blind spot in public perception. *Pacific Conservation Biology*, 19(4), 233-249. https://doi.org/10.1071/pc130233

Mair, L., & Ruete, A. (2016). Explaining Spatial Variation in the Recording Effort of Citizen Science Data across Multiple Taxa. *PLoS ONE*, 11(1), e0147796. https://doi.org/10.1371/journal.pone.0147796

Makarieva, A. M., Gorshkov, V. G., & Li, B. L. (2005). Why do population density and inverse home range scale differently with body size?: Implications for ecosystem stability. *Ecological Complexity*, 2(3), 259-271. https://doi.org/10.1016/j.ecocom.2005.04.006

Malo, J. E., Suárez, F., & Díez, A. (2004). Can we mitigate animal-vehicle accidents using predictive models? *Journal of Applied Ecology*, 41(4), 701-710. https://doi.org/10.1111/j.0021-8901.2004.00929.x

Manneri, A. (2002). Traffic mortality of small- and medium-sized vertebrates in finland. *Tiehallinnon selvityksia, finnra reports*, 26/2002(TIEH 3200758). https://trid.trb.org/view/685666
Mata, C., Hervás, I., Herranz, J., Suárez, F., & Malo, J. E. (2008). Are motorway wildlife passages worth building? Vertebrate use of road-crossing structures on a Spanish motorway. *Journal of Environmental Management*, 88(3), 407-415. https://doi.org/10.1016/j.jenvman.2007.03.014

McLane, A. J., Semeniuk, C., McDermid, G. J., & Marceau, D. J. (2011). The role of agent-based models in wildlife ecology and management. *Ecological Modelling*, 222(8), 1544-1556.
https://doi.org/10.1016/j.ecolmodel.2011.01.020

Meek, R. (2009). Patterns of reptile road-kills in the Vendée region of western France. *Herpetological Journal*, 19(3), 135-142.

Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J., & Schipper, A. M. (2018). Global patterns of current and future road infrastructure. *Environmental Research Letters*, 13(6), 064006.
https://doi.org/10.1088/1748-9326/aabd42

Mengersen, K., Peterson, E. E., Clifford, S., Ye, N., Kim, J., Bednarz, T., Brown, R., James, A., Vercelloni, J., Pearse, A. R., Davis, J., & Hunter, V. (2017). Modelling imperfect presence data obtained by citizen science. *Environmetrics*, 28(5), e2446. https://doi.org/10.1002/env.2446

Merry, K., & Bettinger, P. (2019). Smartphone GPS accuracy study in an urban environment. *PLoS ONE*, 14(7), e0219890. https://doi.org/10.1371/journal.pone.0219890

Mestre, F., Lopes, H., Pinto, T., Sousa, L. G., Mira, A., & Santos, S. M. (2019). Bad moon rising? The influence of the lunar cycle on amphibian roadkills. *European Journal of Wildlife Research*, 65(4), 58.
https://doi.org/10.1007/s10344-019-1285-3

Milanesi, P., Mori, E., & Menchetti, M. (2020). Observer-oriented approach improves species distribution models from citizen science data. *Ecology and Evolution*, 10(21), 12104-12114.
https://doi.org/10.1002/ece3.6832

Miller, M. J., & Loaiza, J. R. (2015). Geographic Expansion of the Invasive Mosquito Aedes albopictus across Panama—Implications for Control of Dengue and Chikungunya Viruses. *PLoS Neglected Tropical Diseases*, 9(1), e0003383. https://doi.org/10.1371/journal.pntd.0003383

Ministère de la transition écologique et de la cohésion des territoires (2022). *Se déplacer en voiture : seul, à plusieurs ou en covoiturage* ? ISSN 2557-8510.
https://www.ecologie.gouv.fr/sites/default/files/DE_4p_covoiturage-v4-050722_SH_OK.pdf

Mokany, K., & Ferrier, S. (2011). Predicting impacts of climate change on biodiversity : A role for semi-mechanistic community-level modelling. *Diversity and Distributions*, 17(2), 374-380.
https://doi.org/10.1111/j.1472-4642.2010.00735.x

Moore, L. J., Petrovan, S. O., Baker, P. J., Bates, A. J., Hicks, H. L., Perkins, S. E., & Yarnell, R. W. (2020). Impacts and Potential Mitigation of Road Mortality for Hedgehogs in Europe. *Animals*, 10(9), Art. 9. https://doi.org/10.3390/ani10091523
Moore, L. J., Petrovan, S. O., Bates, A. J., Hicks, H. L., Baker, P. J., Perkins, S. E., & Yarnell, R. W. (2023). Demographic effects of road mortality on mammalian populations: A systematic review. *Biological Reviews*. https://doi.org/10.1111/brv.12942

Mumme, R. L., Schoech, S. J., Woolfenden, G. E., & Fitzpatrick, J. W. (2000). Life and Death in the Fast Lane: Demographic Consequences of Road Mortality in the Florida Scrub-Jay. *Conservation Biology*, 14(2), 501-512. https://doi.org/10.1046/j.1523-1739.2000.98370.x

Murphy, A., & Xia, J. (Cecilia). (2016). Risk analysis of animal-vehicle crashes: A hierarchical Bayesian approach to spatial modelling. *International Journal of Crashworthiness*, 21(6), 614-626.
https://doi.org/10.1080/13588265.2016.1209823

Nathan, R., Safriel, U. N., & Noy-Meir, I. (2001). Field Validation and Sensitivity Analysis of a Mechanistic Model for Tree Seed Dispersal by Wind. *Ecology*, 82(2), 374-388.
https://doi.org/10.1890/0012-9658(2001)082[0374:FVASAO]2.0.CO;2

Newman, G., Chandler, M., Clyde, M., McGreavy, B., Haklay, M., Ballard, H., Gray, S., Scarpino, R., Hauptfeld, R., Mellor, D., & Gallo, J. (2017). Leveraging the power of place in citizen science for effective conservation decision making. *Biological Conservation*, 208, 55-64.
https://doi.org/10.1016/j.biocon.2016.07.019

Newmark, W. D., Boshe, J. I., Sariko, H. I., & Makumbule, G. K. (1996). Effects of a highway on large mammals in Mikumi National Park, Tanzania. *African Journal of Ecology*, 34(1), 15-31.
https://doi.org/10.1111/j.1365-2028.1996.tb00590.x

Nimmo, D. G., Avitabile, S., Banks, S. C., Bliege Bird, R., Callister, K., Clarke, M. F., Dickman, C. R., Doherty, T. S., Driscoll, D. A., Greenville, A. C., Haslem, A., Kelly, L. T., Kenny, S. A., Lahoz-Monfort, J. J., Lee, C., Leonard, S., Moore, H., Newsome, T. M., Parr, C. L., ... Bennett, A. F. (2019). Animal movements in fire-prone landscapes. *Biological Reviews*, 94(3), 981-998.
https://doi.org/10.1111/brv.12486

Nunes, L. J. R., Meireles, C. I. R., C.j, P. G., & N.m.c, A. R. (2020). Propagation Model of Invasive Species: Road Systems as Dispersion Facilitators. *Research in Ecology*, 2(1), Art. 1.
https://doi.org/10.30564/re.v2i1.1054

Oliver, R. M. (1961). A Traffic Counting Distribution. *Operations Research*, 9(6), 802-810.
https://doi.org/10.1287/opre.9.6.802

Olson, D. D., Bissonette, J. A., Cramer, P. C., Green, A. D., Davis, S. T., Jackson, P. J., & Coster, D. C. (2014). Monitoring Wildlife-Vehicle Collisions in the Information Age: How Smartphones Can Improve Data Collection. *PLoS ONE*, 9(6), e98613. https://doi.org/10.1371/journal.pone.0098613

Oxley, D. J., Fenton, M. B., & Carmody, G. R. (1974). The Effects of Roads on Populations of Small Mammals. *Journal of Applied Ecology*, 11(1), 51-59. https://doi.org/10.2307/2402004

Padmanaba, M., & Sheil, D. (2014). Spread of the Invasive Alien Species *Piper Aduncum* via Logging Roads in Borneo. *Tropical Conservation Science*, 7(1), 35-44.
https://doi.org/10.1177/194008291400700108
Pagany, R. (2020). Wildlife-vehicle collisions—Influencing factors, data collection and research methods. *Biological Conservation*, 251(August), 108758. https://doi.org/10.1016/j.biocon.2020.108758

Paul, K., Quinn, M. S., Huijser, M. P., Graham, J., & Broberg, L. (2014). An evaluation of a citizen science data collection program for recording wildlife observations along a highway. *Journal of Environmental Management*, 139, 180-187. https://doi.org/10.1016/j.jenvman.2014.02.018

Pellissier, V., Schmucki, R., Pe'er, G., Aunins, A., Brereton, T. M., Brotons, L., Carnicer, J., Chodkiewicz, T., Chylarecki, P., del Moral, J. C., Escandell, V., Evans, D., Foppen, R., Harpke, A., Heliölä, J., Herrando, S., Kuussaari, M., Kühn, E., Lehikoinen, A., ... Julliard, R. (2020). Effects of Natura 2000 on nontarget bird and butterfly species based on citizen science data. *Conservation Biology*, 34(3), 666-676. https://doi.org/10.1111/cobi.13434

Périquet, S., Roxburgh, L., le Roux, A., & Collinson, W. J. (2018). Testing the value of citizen science for roadkill studies : A case study from South Africa. *Frontiers in Ecology and Evolution*, 6(FEB), 1-9. https://doi.org/10.3389/fevo.2018.00015

Peters, R. H., & Wassenberg, K. (1983). The effect of body size on animal abundance. *Oecologia*, 60(1), 89-96. https://doi.org/10.1007/BF00379325

Peterson, A. T., Papeş, M., & Soberón, J. (2015). Mechanistic and Correlative Models of Ecological Niches. *European Journal of Ecology*, 1(2), 28-38. https://doi.org/10.1515/eje-2015-0014

Petrovan, S. O., Vale, C. G., & Sillero, N. (2020). Using citizen science in road surveys for large-scale amphibian monitoring : Are biased data representative for species distribution? *Biodiversity and Conservation*, 29(6), 1767-1781. https://doi.org/10.1007/s10531-020-01956-0

Plante, J., Jaeger, J. A. G., & Desrochers, A. (2019). How do landscape context and fences influence roadkill locations of small and medium-sized mammals? *Journal of Environmental Management*, 235, 511-520. https://doi.org/10.1016/j.jenvman.2018.10.093

Pojar, T. M., Prosence, R. A., Reed, D. F., & Woodard, T. N. (1975). Effectiveness of a Lighted, Animated Deer Crossing Sign. *The Journal of Wildlife Management*, 39(1), 87-91. https://doi.org/10.2307/3800469

Pokorny, B., Cerri, J., & Bužan, E. (2021). Roadkill in a time of pandemic : The analysis of wildlife-vehicle collisions reveals the differential impact of COVID-19 lockdown over mammal assemblages. *EcoEvoRxiv*. https://doi.org/10.32942/osf.io/p3zft

Potts, J. R., & Lewis, M. A. (2014). How do animal territories form and change? Lessons from 20 years of mechanistic modelling. *Proceedings of the Royal Society B: Biological Sciences*, 281(1784), 20140231. https://doi.org/10.1098/rspb.2014.0231

Proença, V., Martin, L. J., Pereira, H. M., Fernandez, M., McRae, L., Belnap, J., Böhm, M., Brummitt, N., García-Moreno, J., Gregory, R. D., Honrado, J. P., Jürgens, N., Opige, M., Schmeller, D. S., Tiago, P., & van Swaay, C. A. M. (2017). Global biodiversity monitoring : From data sources to Essential Biodiversity Variables. *Biological Conservation*, 213, 256-263. https://doi.org/10.1016/j.biocon.2016.07.014
Prunier, J. G., Kaufmann, B., Léna, J.-P., Fenet, S., Pompanon, F., & Joly, P. (2014). A 40-year-old divided highway does not prevent gene flow in the alpine newt *Ichthyosaura alpestris*. *Conservation Genetics*, 15(2), 453-468. https://doi.org/10.1007/s10592-013-0553-0

Pyke, G. H., Pulliam, H. R., & Charnov, E. L. (1977). Optimal Foraging: A Selective Review of Theory and Tests. *The Quarterly Review of Biology*, 52(2), 137-154. https://doi.org/10.1086/409852

Quiles, P. (2018). Are road-kills representative of wildlife community obtained from atlas data? *Hystrix*, *the Italian Journal of Mammalogy*, 29(2), 190-194. https://doi.org/10.4404/hystrix

Rastetter, E. B., Aber, J. D., Peters, D. P. C., Ojima, D. S., & Burke, I. C. (2003). Using Mechanistic Models to Scale Ecological Processes across Space and Time. *BioScience*, 53(1), 68-76.
https://doi.org/10.1641/0006-3568(2003)053[0068:UMMTSE]2.0.CO;2

Raymond, S., Schwartz, A. L. W., Thomas, R. J., Chadwick, E., & Perkins, S. E. (2021). Temporal patterns of wildlife roadkill in the UK. *PLoS ONE*, 16(10), e0258083.
https://doi.org/10.1371/journal.pone.0258083

Reid, L. M., & Dunne, T. (1984). Sediment production from forest road surfaces. *Water Resources Research*, 20(11), 1753-1761. https://doi.org/10.1029/WR020i011p01753

Reijnen, R., Foppen, R., Braak, C. T., & Thissen, J. (1995). The Effects of Car Traffic on Breeding Bird Populations in Woodland. III. Reduction of Density in Relation to the Proximity of Main Roads. *Journal of Applied Ecology*, 32(1), 187-202. https://doi.org/10.2307/2404428

Reynolds, C. W. (1987). Flocks, herds and schools : A distributed behavioral model. *Proceedings of the 14th annual conference on Computer graphics and interactive techniques*, 25-34.
https://doi.org/10.1145/37401.37406

Riaz, F., & Niazi, M. A. (2016). Road collisions avoidance using vehicular cyber-physical systems : A taxonomy and review. *Complex Adaptive Systems Modeling*, 4(1), 15. https://doi.org/10.1186/s40294-016-0025-8

Rigoudy, N., Dussert, G., Benyoub, A., Besnard, A., Birck, C., Boyer, J., Bollet, Y., Bunz, Y., Caussimont, G., Chetouane, E., Carriburu, J. C., Cornette, P., Delestrade, A., Backer, N. D., Dispan, L., Barh, M. L., Duhayer, J., Elder, J.-F., Fanjul, J.-B., ... Chamaillé-Jammes, S. (2023). The DeepFaune initiative : A collaborative effort towards the automatic identification of French fauna in camera-trap images (p. 2022.03.15.484324). *bioRxiv*. https://doi.org/10.1101/2022.03.15.484324

Riley, S. P. D., Pollinger, J. P., Sauvajot, R. M., York, E. C., Bromley, C., Fuller, T. K., & Wayne, R. K. (2006). FAST-TRACK : A southern California freeway is a physical and social barrier to gene flow in carnivores. *Molecular Ecology*, 15(7), 1733-1741. https://doi.org/10.1111/j.1365-294X.2006.02907.x

Robertson, M. P., Peter, C. I., Villet, M. H., & Ripley, B. S. (2003). Comparing models for predicting species' potential distributions : A case study using correlative and mechanistic predictive modelling techniques. *Ecological Modelling*, 164(2-3), 153-167. https://doi.org/10.1016/S0304-3800(03)00028-0
Robinson, O. J., Ruiz-Gutierrez, V., & Fink, D. (2018). Correcting for bias in distribution modelling for rare species using citizen science data. *Diversity and Distributions*, 24(4), 460-472.
https://doi.org/10.1111/ddi.12698

Rotholz, E., & Mandelik, Y. (2013). Roadside habitats: Effects on diversity and composition of plant, arthropod, and small mammal communities. *Biodiversity and Conservation*, 22(4), 1017-1031.
https://doi.org/10.1007/s10531-013-0465-9

Rytwinski, T., Soanes, K., Jaeger, J. A. G., Fahrig, L., Findlay, C. S., Houlahan, J., Van Ree, R. D., & Van Der Grift, E. A. (2016). How effective is road mitigation at reducing road-kill? A meta-analysis. *PLoS ONE*, 11(11), 1-25.
https://doi.org/10.1371/journal.pone.0166941

Ryu, M., & Kim, J. G. (2021). Coastal road mortality of land crab during spawning migration. *Scientific Reports*, 11(1), Art. 1. https://doi.org/10.1038/s41598-021-86143-z

Saad, W., & Alsayyari, A. S. (2020). Real Time IoT Based Camel-Vehicle Collision Avoidance System for KSA. 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), 1-4.
https://doi.org/10.1109/ICCAIS48893.2020.9096714

Saint-Andrieux, C., Calenge, C., & Bonenfant, C. (2020). Comparison of environmental, biological and anthropogenic causes of wildlife-vehicle collisions among three large herbivore species. *Population Ecology*, 62(1), 64-79.
https://doi.org/10.1002/1438-390X.12029

Santos, R. A. L., & Ascensão, F. (2019). Assessing the effects of road type and position on the road on small mammal carcass persistence time. *European Journal of Wildlife Research*, 65(1).
https://doi.org/10.1007/s10344-018-1246-2

Santos, R. A. L., Mota-Ferreira, M., Aguiar, L. M. S., & Ascensão, F. (2018). Predicting wildlife road-crossing probability from roadkill data using occupancy-detection models. *Science of The Total Environment*, 642, 629-637.
https://doi.org/10.1016/j.scitotenv.2018.06.107

Santos, R. A. L., Santos, S. M., Santos-Reis, M., Picanço de Figueiredo, A., Bager, A., Aguiar, L. M. S., & Ascensão, F. (2016). Carcass Persistence and Detectability: Reducing the Uncertainty Surrounding Wildlife-Vehicle Collision Surveys. *PLoS ONE*, 11(11), e0165608.
https://doi.org/10.1371/journal.pone.0165608

Santos, S. M., Carvalho, F., & Mira, A. (2011). How long do the dead survive on the road? Carcass persistence probability and implications for road-kill monitoring surveys. *PLoS ONE*, 6(9).
https://doi.org/10.1371/journal.pone.0025383

Santos, S. M., Lourenço, R., Mira, A., & Beja, P. (2013). Relative Effects of Road Risk, Habitat Suitability, and Connectivity on Wildlife Roadkills: The Case of Tawny Owls (*Strix aluco*). *PLoS ONE*, 8(11), e79967.
https://doi.org/10.1371/journal.pone.0079967

Sauermann, H., & Franzoni, C. (2015). Crowd science user contribution patterns and their implications. *Proceedings of the National Academy of Sciences*, 112(3), 679-684.
https://doi.org/10.1073/pnas.1408907112
Saxena, A., Gupta, D. K., & Singh, S. (2021). An Animal Detection and Collision Avoidance System Using Deep Learning. In G. S. Hura, A. K. Singh, & L. Siong Hoe (Éds.), *Advances in Communication and Computational Technology* (p. 1069-1084). Springer Nature. https://doi.org/10.1007/978-981-15-5341-7_81

Schmeller, D. S., Henry, P.-Y., Julliard, R., Gruber, B., Clobert, J., Dziock, F., Lengyel, S., Nowicki, P., Déri, E., Budrys, E., Kull, T., Tali, K., Bauch, B., SetTELe, J., Van Swaay, C., Kobler, A., Babij, V., Papastergiadou, E., & Henle, K. (2009). Advantages of Volunteer-Based Biodiversity Monitoring in Europe. *Conservation Biology*, 23(2), 307-316. https://doi.org/10.1111/j.1523-1739.2008.01125.x

Schmidt, B. R., & Zumbach, S. (2008). Amphibian Road Mortality and How to Prevent It: A Review. *Herpetological Conservation*, 3 (Cha, Art. 3)

Schwartz, A. L. W., Williams, H. F., Chadwick, E., Thomas, R. J., & Perkins, S. E. (2018). Roadkill scavenging behaviour in an urban environment. *Journal of Urban Ecology*, 4(1), 1-7.
https://doi.org/10.1093/jue/juy006

Seburn, D. C., & McCurdy-Adams, H. (2019). Do turtle warning signs reduce roadkill? *The Canadian Field-Naturalist*, 133(3), Art. 3. https://doi.org/10.22621/cfn.v133i3.2279

Secco, H., Ratton, P., Castro, E., da Lucas, P. S., & Bager, A. (2014). Intentional Snake Road-Kill: A Case Study using Fake Snakes on a Brazilian Road. *Tropical Conservation Science*, 7(3), 561-571. https://doi.org/10.1177/194008291400700313

Seiler, A. (2001). Ecological Effects of Roads: A review. *Uppsala: Swedish University of Agricultural Sciences*.

Seiler, A. (2005). Predicting locations of moose-vehicle collisions in Sweden. *Journal of Applied Ecology*, 42(2), 371-382. https://doi.org/10.1111/j.1365-2664.2005.01013.x

Seiler, A., & Helldin, J. O. (2006). Mortality in wildlife due to transportation. In *The ecology of transportation: Managing mobility for the environment* (pp. 165-189). Dordrecht: Springer Netherlands.

Selvan, M. (2012). Roadkill animals on national highways of Karnataka, India. *Journal of Ecology and The Natural Environment*, 4(14), 363-365. https://doi.org/10.5897/JENE11.068

Semlitsch, R. D., Ryan, T. J., Hamed, K., Chatfield, M., Drehman, B., Pekarek, N., Spath, M., & Watland, A. (2007). Salamander abundance along road edges and within abandoned logging roads in Appalachian forests. *Conservation Biology: The Journal of the Society for Conservation Biology*, 21(1), 159-167. https://doi.org/10.1111/j.1523-1739.2006.00571.x

Serieys, L. E. K., Rogan, M. S., Matsushima, S. S., & Wilmers, C. C. (2021). Road-crossings, vegetative cover, land use and poisons interact to influence corridor effectiveness. *Biological Conservation*, 253, 108930. https://doi.org/10.1016/j.biocon.2020.108930

Shanley, C. S., & Pyare, S. (2011). Evaluating the road-effect zone on wildlife distribution in a rural landscape. *Ecosphere*, 2(2), art16. https://doi.org/10.1890/ES10-00093.1
Sharma, S. U., & Shah, D. J. (2017). A Practical Animal Detection and Collision Avoidance System Using Computer Vision Technique. *IEEE Access*, 5, 347-358.

https://doi.org/10.1109/ACCESS.2016.2642981

Shi, X. Z., Wang, K., Warner, E. D., Yu, D. S., Wang, H. J., Yang, R. W., Liang, Y., & Shi, D. M. (2008).

Relationship between soil erosion and distance to roadways in undeveloped areas of China. *CATENA*, 72(2), 305-313. https://doi.org/10.1016/j.catena.2007.06.004

Shilling, F., Collinson, W., Bil, M., Vercayie, D., Heigl, F., Perkins, S. E., & MacDougall, S. (2020). Designing wildlife-vehicle conflict observation systems to inform ecology and transportation studies.

*Biological Conservation*, 251, 108797.

Shilling, F. M., & Waetjen, D. P. (2015). Wildlife-vehicle collision hotspots at US highway extents :
Scale and data source effects. *Nature Conservation*, 11, 41-60.

https://doi.org/10.3897/natureconservation.11.4438

Shin, Y., Kim, K., Groffen, J., Woo, D., Song, E., & Borzée, A. (2022). Citizen science and roadkill trends in the Korean herpetofauna: The importance of spatially biased and unstandardized data.

*Frontiers in Ecology and Evolution*, 10. https://www.frontiersin.org/articles/10.3389/fevo.2022.944318

Sicacha-Parada, J., Steinsland, I., Cretois, B., & Borgelt, J. (2021). Accounting for spatial varying sampling effort due to accessibility in Citizen Science data: A case study of moose in Norway. *Spatial Statistics*, 42, 100446. https://doi.org/10.1016/j.spasta.2020.100446

Sidorovich, A. A. (2020). Road Mortality of Carnivores (*Mammalia, Carnivora*) in Belarus. *Zoodiversity*,

54(3), 211-220. https://doi.org/10.15407/zoo2020.03.211

Sijtsma, F. J., van der Veen, E., van Hinsberg, A., Pouwels, R., Bekker, R., van Dijk, R. E., Grutters, M., Klaassen, R., Krijn, M., Mouissie, M., & Wymenga, E. (2020). Ecological impact and cost-effectiveness of wildlife crossings in a highly fragmented landscape: A multi-method approach. *Landscape Ecology*,

35(7), 1701-1720. https://doi.org/10.1007/s10980-020-01047-z

Skórka, P., Lenda, M., Moroń, D., Martyka, R., Tryjanowski, P., & Sutherland, W. J. (2015). Biodiversity collision blackspots in Poland: Separation causality from stochasticity in roadkills of butterflies.

*Biological Conservation*, 187, 154-163. https://doi.org/10.1016/j.biocon.2015.04.017

Slater, S. J., Maloney, D. M., & Taylor, J. M. (2022). Golden eagle use of winter roadkill and response to vehicles in the western United States. *The Journal of Wildlife Management*, 86(6), e22246.

https://doi.org/10.1002/jwmg.22246

Spellerberg, I. (1998). Ecological effects of roads and traffic: A literature review. *Global Ecology & Biogeography Letters*, 7(5), 317-333. https://doi.org/10.1046/j.1466-822x.1998.00308.x

Steiner, W., Leisch, F., & Hackländer, K. (2014). A review on the temporal pattern of deer-vehicle accidents: Impact of seasonal, diurnal and lunar effects in cervids. *Accident Analysis and Prevention*, 66,

168-181. https://doi.org/10.1016/j.aap.2014.01.020
Steiner, W., Schöll, E. M., Leisch, F., & Hackländer, K. (2021). Temporal patterns of roe deer traffic accidents: Effects of season, daytime and lunar phase. *PLoS ONE*, 16(3), e0249082.
https://doi.org/10.1371/journal.pone.0249082

Stevenson, E. R., Chitwood, M. C., Lashley, M. A., Pollock, K. H., Swingen, M. B., Moorman, C. E., & DePerno, C. S. (2016). Survival and Cause-Specific Mortality of Coyotes on a Large Military Installation. *Southeastern Naturalist*, 15(3), 459-466. https://doi.org/10.1656/058.015.0307

Středová, H., Podhrázská, J., Chuchma, F., Středa, T., Kučera, J., Fukalová, P., & Blecha, M. (2021). The Road Map to Classify the Potential Risk of Wind Erosion. *ISPRS International Journal of Geo-Information*, 10(4), Art. 4. https://doi.org/10.3390/ijgi10040269

Swanson, K., Land, D., Kautz, R., & Kawula, R. (2008). Use of least-cost pathways to identify key road segments for Florida panther conservation. *Florida Fish and Wildlife Conservation Commission, Fish and Wildlife Research Institute*

Swinnen, K. R., Jacobs, A., Claus, K., Ruyts, S., Vercayie, D., Lambrechts, J., & Herremans, M. (2022). ‘Animals under wheels’: Wildlife roadkill data collection by citizen scientists as a part of their nature recording activities. *Nature Conservation*, 47, 121-153.

Tatewaki, T., & Koike, F. (2018). Synoptic scale mammal density index map based on roadkill records. *Ecological Indicators*, 85, 468-478. https://doi.org/10.1016/j.ecolind.2017.10.056

Taylor, P. D., Fahrig, L., Henein, K., & Merriam, G. (1993). Connectivity Is a Vital Element of Landscape Structure. *Oikos*, 68(3), 571. https://doi.org/10.2307/3544927

Taylor, J. (2022). Roadkill Encounters and Affective Solidarity: Exploring Multispecies Fellowship through Disgust, Pain, and Mourning. *Sociální Studia / Social Studies*, 19(1), Art. 1.
https://doi.org/10.5817/SOC2022-29553

Teixeira, F. Z., Coelho, A. V. P., Esperandio, I. B., & Kindel, A. (2013). Vertebrate road mortality estimates: Effects of sampling methods and carcass removal. *Biological Conservation*, 157, 317-323.
https://doi.org/10.1016/j.biocon.2012.09.006

Teixeira, F. Z., Kindel, A., Hartz, S. M., Mitchell, S., & Fahrig, L. (2017). When road-kill hotspots do not indicate the best sites for road-kill mitigation. *Journal of Applied Ecology*, 54(5), 1544-1551.
https://doi.org/10.1111/1365-2664.12870

Tennessen, J. B., Parks, S. E., & Langkilde, T. (2014). Traffic noise causes physiological stress and impairs breeding migration behaviour in frogs. *Conservation Physiology*, 2(1), cou032.
https://doi.org/10.1093/conphys/cou032

Testud, G., Fauconnier, C., Labarraque, D., Lengagne, T., Le Petitcorps, Q., Picard, D., & Miaud, C. (2020). Acoustic enrichment in wildlife passages under railways improves their use by amphibians. *Global Ecology and Conservation*, 24, e01252. https://doi.org/10.1016/j.gecco.2020.e01252
Thel, L., Chamaillé-Jammes, S., Keurinck, L., Catala, M., Packer, C., Huebner, S. E., & Bonenfant, C. (2021). Can citizen science analysis of camera trap data be used to study reproduction? Lessons from Snapshot Serengeti program. *Wildlife Biology*, 2021(2), wlb.00833. https://doi.org/10.2981/wlb.00833

Thompson, B. K., Olden, J. D., & Converse, S. J. (2021). Mechanistic invasive species management models and their application in conservation. *Conservation Science and Practice*, 3(11), e533.
https://doi.org/10.1111/csp2.533

Thompson, M. M., Moon, K., Woods, A., Rowley, J. J. L., Poore, A. G. B., Kingsford, R. T., & Callaghan, C. T. (2023). Citizen science participant motivations and behaviour: Implications for biodiversity data coverage. *Biological Conservation*, 282, 110079.
https://doi.org/10.1016/j.biocon.2023.110079

Tiago, P., Pereira, H. M., & Capinha, C. (2017). Using citizen science data to estimate climatic niches and species distributions. *Basic and Applied Ecology*, 20, 75-85.
https://doi.org/10.1016/j.baae.2017.04.001

Timothy, N., & Tagui Williams, E. (2019). Environmental Pollution by Heavy Metal: An Overview. *International Journal of Environmental Chemistry*, 3(2), 72. https://doi.org/10.11648/j.ijec.20190302.14

Valentin, C., Poesen, J., & Li, Y. (2005). Gully erosion: Impacts, factors and control. *CATENA*, 63(2), 132-153. https://doi.org/10.1016/j.catena.2005.06.001

Van der Ree, R., Jaeger, J. A. G., van der Grift, E. A., & Clevenger, A. P. (2011). Effects of roads and traffic on wildlife populations and landscape function: Road ecology is moving toward larger scales. *Ecology and Society*, 16(1). https://doi.org/10.5751/ES-03982-160148

Van Eupen, C., Maes, D., Herremans, M., Swinnen, K. R. R., Somers, B., & Luca, S. (2022). Species profiles support recommendations for quality filtering of opportunistic citizen science data. *Ecological Modelling*, 467, 109910. https://doi.org/10.1016/j.ecolmodel.2022.109910

Van Langevelde, F., & Jaarsma, C. F. (2005). Using traffic flow theory to model traffic mortality in mammals. *Landscape Ecology*, 19(8), 895-907. https://doi.org/10.1007/s10980-005-0464-7

Van Langevelde, F., van Dooremalen, C., & Jaarsma, C. F. (2009). Traffic mortality and the role of minor roads. *Journal of Environmental Management*, 90(1), 660-667.
https://doi.org/10.1016/j.jenvman.2007.09.003

Ver Hoef, J. M., Johnson, D., Angliss, R., & Higham, M. (2021). Species density models from opportunistic citizen science data. *Methods in Ecology and Evolution*, 12(10), 1911-1925.
https://doi.org/10.1111/2041-210X.13679

Vercayie, D., & Herremans, M. (2015). Citizen science and smartphones take roadkill monitoring to the next level. *Nature Conservation*, 11, 29-40. https://doi.org/10.3897/natureconservation.11.4439

Villalobos-Hoffman, R., Ewing, J. E., & Mooring, M. S. (2022). Do Wildlife Crossings Mitigate the Roadkill Mortality of Tropical Mammals? A Case Study from Costa Rica. *Diversity*, 14(8), Art. 8.
https://doi.org/10.3390/d14080665
Viscido, S. V., Miller, M., & Wethey, D. S. (2001). The Response of a Selfish Herd to an Attack from Outside the Group Perimeter. *Journal of Theoretical Biology*, 208(3), 315-328.
https://doi.org/10.1006/jtbi.2000.2221

Waetjen, D. P., & Shilling, F. M. (2017). Large Extent Volunteer Roadkill and Wildlife Observation Systems as Sources of Reliable Data. *Frontiers in Ecology and Evolution, 5*.
https://www.frontiersin.org/articles/10.3389/fevo.2017.00089

Walker, D. A., & Everett, K. R. (1987). Road Dust and its Environmental Impact on Alaskan Taiga and Tundra. *Arctic and Alpine Research, 19*(4), 479-489. https://doi.org/10.1080/00040851.1987.12002630

Walper, K. H., Ellenberg, H., Mueller, K., & Stottele, T. (1981). Ausgangssituationen-strassen-oekologie-auswirkungen von autobahnen und strassen auf oekosysteme deutscher landschaften. *Ökologie und Straße*.

Wang, Y., Yang, Y., Han, Y., Shi, G., Zhang, L., Wang, Z., Cao, G., Zhou, H., Kong, Y., Piao, Z., & Merrow, J. (2022). Temporal patterns and factors influencing vertebrate roadkill in China. *Transportation Research Interdisciplinary Perspectives*, 15, 100662. https://doi.org/10.1016/j.trip.2022.100662

White, E. P., Ernest, S. K. M., Kerkhoff, A. J., & Enquist, B. J. (2007). Relationships between body size and abundance in ecology. *Trends in Ecology & Evolution, 22*(6), 323-330.
https://doi.org/10.1016/j.tree.2007.03.007

Willi, M., Pitman, R. T., Cardoso, A. W., Locke, C., Swanson, A., Boyer, A., Veldthuis, M., & Fortson, L. (2019). Identifying animal species in camera trap images using deep learning and citizen science. *Methods in Ecology and Evolution, 10*(1), 80-91. https://doi.org/10.1111/2041-210X.13099

Williams, S. T., Collinson, W., Patterson-Abrolat, C., Marneweck, D. G., & Swanepoel, L. H. (2019). Using road patrol data to identify factors associated with carnivore roadkill counts. *PeerJ*, 7, e6650.
https://doi.org/10.7717/peerj.6650

Woltz, H. W., Gibbs, J. P., & Ducey, P. K. (2008). Road crossing structures for amphibians and reptiles : Informing design through behavioral analysis. *Biological Conservation*, 141(11), 2745-2750.
https://doi.org/10.1016/j.biocon.2008.08.010

Yue, S., Bonebrake, T. C., & Gibson, L. (2019). Informing snake roadkill mitigation strategies in Taiwan using citizen science. *The Journal of Wildlife Management*, 83(1), 80-88.
https://doi.org/10.1002/jwmg.21580

Yulevitch, G., Danon, M., Krasovitov, B., Fominykh, A., Swet, N., Tsesarsky, M., & Katra, I. (2020). Evaluation of wind-induced dust-PM emission from unpaved roads varying in silt content by experimental results. *Atmospheric Pollution Research*, 11(2), 261-268.
https://doi.org/10.1016/j.apr.2019.10.010

Zhang, W., Shu, G., Li, Y., Xiong, S., Liang, C., & Li, C. (2018). Daytime driving decreases amphibian roadkill. *PeerJ*, 6, e5385. https://doi.org/10.7717/peerj.5385