number of toad roadkill of about -50%, for a survey conducted 3 hours after amphibian crossing at this
particular location. A similar survey conducted the next morning (t=12h after road crossing window)
would have under-estimated the casualties by about 70%. Aquatic breeding amphibians are particularly
suited to this example of roadkill counts correction because roadkill is synchronized at sunset, allowing
for estimations of the time elapsed between the road crossings and the survey, and because road crossings
are localized in space, allowing for on-foot surveys where the detection rate of carcasses is higher than
surveys done by car.

Correcting roadkill estimates for carcass persistence is much less straightforward when animal-vehicle
collisions happen continuously. Teixeira et al. (2013) developed a theoretical framework of carcass
persistence correction for roadkill monitoring surveys which we applied to citizen science data of
passerine roadkill. We estimated a total of 7000 collisions between small passerines and vehicles in the
AuRA region. Not all small passerine species present in AuRA were present in the roadkill reports for
2022, meaning that this number is likely under-estimated. Roadkill rates ranged from 0.9 (house sparrow
*Passer domesticus* and European robin *Erithacus rubecula*) to 0.01 (long-tailed tit *Aegithalos caudatus*)
collision per 100km of road and per year. Roadkill rates are likely linked to the local abundance of
species (Seiler & Helldin, 2006). Despite having no current estimate of these species' abundance in the
AuRA region, *P. domesticus* and *E. rubecula* are the most reported small passerines in Faune-AuRA, and
they are reported 5 times more than *A. caudatus* (resp. 562 577, 681 000 and 164 600 direct observations
of living specimens between 2000 and 2023). This shows that patterns of species abundance in the AuRA
region could be a driving factor in the inter-specific differences in roadkill rates, along with species traits
and behaviors (Grilo et al., 2020; Teixeira et al., 2013).

However, while we estimate the persistence time on the road for these species, we have little information
about the conversion rate of roadkill carcasses into reported roadkill. Indeed, following a collision with a
vehicle a passerine might never be detected by contributors (detection probability) or be detected but
never reported (reporting probability), because they forgot or could not identify the species with
certainty. Additionally, an unknown proportion of passerines could be projected into the roadside
vegetation at the moment of impact. Very little work has been done on roadkill detection, although we
know that detection rates of roadkill from a car (which encompasses the majority of reports into Faune-
AuRA) is significantly lower than when surveying carcasses on foot (Teixeira et al., 2013). Reporting
probabilities in citizen science roadkill projects are even less documented. We estimated the cumulative
number passerine roadkill for the 21 reported species using a range of plausible values for the detection
and reporting probabilities in figure A2. Using an estimate of 1% of report for passerine collisions with
vehicles (i.e., 1% of the passerines killed by a vehicle are detected, identified and reported by a
contributor) and after correcting for passerine persistence time on the road, we estimate about 700 000 small passerines killed by vehicles in 2022 in the AuRA region, a surface of 70 000 km². In other words, the roadkill citizen science reports only give us a glimpse into a much larger issue, as we multiplied the number of reports by a factor of 3500. Currently, no estimates of the abundance of passerine species in the region are available to put these estimates into perspective. The French Breeding Bird Survey (a standardized protocol for bird populations tracking, Julliard & Jiguet, 2002) reports alarming declines of up to -50% in wild small passerine french populations over the last two decades (European greenfinch *Chloris chloris*, -50%; European serin *Serinus serinus*, -41.7%; yellowhammer *Emberiza citrinella*, -53.6%). Although wild passerine populations face multiple anthropogenic threats from farmland practices (Broyer et al., 2016; Moreau et al., 2021; Rigal et al., 2023), road mortality could be an important and under-estimated driver of biodiversity loss that needs investigating.

## Conclusion

By conducting daily or bi-daily surveys, an observer would miss an important part of the road mortality for small-bodied birds and amphibians. Adapting the periodicity of roadkill surveys to minimize the number of missed carcasses is not a viable option (unless we monitor road continuously). Instead, we should focus on quantifying the persistence of carcasses and correct the data gathered during surveys. Our estimations of the number of killed animals by collisions with vehicles illustrate the huge gap between raw counts of carcasses on roads and how many animals were actually lost. Common toads casualties 3 hours after they crossed the road were already underestimated by half, which has severe consequences in the quantification of the impact of road mortality in amphibian biodiversity loss. Assuming a collision-report rate of 1% in the citizen science project *Faune-Auvergne-Rhône-Alpes*, we estimate 700 000 individuals killed each year in a surface of 70 000 km², which could play a part in the national decline in small passerine population observed over the last two decades.

## Acknowledgments

We thank the wildlife rehabilitation center Le Tichodrome for their help in providing small passerine carcasses and Benoit Vallas for his contribution to the data record. We would also like to thank Julien Girard-Claudon and the LPO Auvergne-Rhône-Alpes for their help in acquiring and analyzing biodiversity data, as well as all the Faune-AuRA project contributors.
Funding

This work was supported by French Ministry of Research and Higher Education funding (PhD grant 2020-2023) and the SR3A.
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<figure><img src="image_40.png" /></figure>

Fig. A1: **Survival model predictions of carcass persistence.** We represent the median persistence time (■; time for half of the carcasses to disappear) of amphibian (a, b) and small passerine (c, d) carcasses on roads of different volumes of traffic. We fitted parametric survival models for both taxa, including road traffic volume as one of the covariables. The predictions of the models are represented as black lines (grey lines: 95% confidence interval). Road traffic volume was either modeled as a linear (a, c) or log-linear (b, d) covariable, and we selected the best option based on the visual fit between the median persistence on each road and the model predictions. Amphibian persistence was modeled using a log-linear effect of traffic (b) and passerine persistence was modeled using a linear effect (c).
<figure><img src="image_41.png" /></figure>

Fig. A2: **Passerine roadkill estimates in 2022.** Estimated number of collisions (gray area: 95% confidence interval) between 21 small passerine species (< 20g) and road vehicles in 2022 in the region Auvergne-Rhône-Alpes (AuRA), France, based on reports from citizen science database Faune-AuRA. Roadkill reports were first adjusted to account for carcass persistence time on the road (tested experimentally with fresh carcasses) following Teixeira et al. (2013). Then, following the assumption that only a small proportion of small-bodied carcasses present on the road are both seen and reported by users in citizen science roadkill reporting projects, we show here the estimated number of actual collisions if between 0.5 and 30% of passerines carcasses present on the road surface end up in the database. For example, if 1% of the visible small passerine roadkill is reported, we estimate 709 100 actual collisions in AuRA in 2022.

Complete list of small passerines species reported in Faune-AuRA in 2022: *Aegithalos caudatus*, *Carduelis carduelis*, *Carduelis chloris*, *Cyanistes caeruleus*, *Emberiza cirlus*, *Emberiza citrinella*, *Erithacus rubecula*, *Fringilla coelebs*, *Fringilla montifringilla*, *Motacilla alba*, *Motacilla flava flava*, *Parus major*, *Passer domesticus*, *Phoenicurus ochruros*, *Phoenicurus phoenicurus*, *Phylloscopus trochilus*, *Prunella modularis*, *Saxicola rubetra*, *Saxicola rubicola*, *Serinus serinus*, *Sylvia atricapilla*, *Sylvia borin*, *Sylvia communis*, *Sylvia melanocephala*, *Troglodytes troglodytes*.
<figure><img src="image_42.png" /><figcaption>Fig. A3: **Kaplan-Meier curve of passerine persistence on a road.** We conducted an additional experiment where we placed 12 small passerine carcasses on a dry road surface (8000 veh.day⁻¹) and continuously counted the number of passing vehicles until the carcasses disappeared (i.e. carcass can't be detected on foot during a road survey or species is no longer identifiable). The experiment was conducted on an urban road with a 50 km.h⁻¹ speed limit, during a day with no rainfall. Passerines were placed randomly on the road surface using a random number generator. Half of the carcasses had disappeared after 632 vehicles, and 58% were still visible when the experiment was ended after 2 hours. All carcass disappearances were the result of repeated crushing by vehicle wheels. All birds (100%) were crushed at least once during the experiment, including carcasses placed in parts of the road lane that vehicles were unlikely to reach, either because the turbulence of passing vehicles had displaced the carcasses, or because some vehicles swerved on the lane to avoid obstacles. The rates of carcass removal during the first 2 hours are lower here than in the experiments conducted in the main body of this article, which hints at a possible involvement of other mechanisms of carcass removal beyond mechanical crushing by tires.</figcaption></figure>
# An experimental measurement of carcass persistence time and sources of removal as revealed by camera-traps (in preparation)

Annaëlle Bénard, Thierry Lengagne and Christophe Bonenfant

## CONTEXT

Roadkill carcasses observed on roads are often used as an approximation of road mortality. However, the actual number of wildlife-vehicle collisions is likely higher than the raw count of detected carcasses, because they can disappear from the road within a few days or even hours. Previous estimates of roadkill persistence only have a resolution of 24 hours, and the mechanisms implicated in roadkill removal are deduced without any direct evidence.

## OBJECTIVES

Using camera-traps, continuously track the removal of medium to large bird and mammal carcasses from roads to:

- Refine existing estimates of roadkill persistence,

- Document the mechanisms by which roadkill is removed and explore the interactions between these mechanisms, the species involved, and the volume of traffic.

## KEY RESULTS

- Roadkill was typically removed within an average of 18 hours, mostly through the movement of vehicle tires and the actions of road users.

- The contribution of carrion-feeders to the removal of carcasses appears to be relatively small.

- The most probable mechanism of removal varied depending on the species and the amount of road traffic, but we did not find any statistical evidence that these factors increase or decrease the persistence time.

## CONCLUSIONS

Short persistence time (< 24h) of roadkill suggests that even daily monitoring of roads may underestimate the true extent of wildlife-vehicle collisions. Furthermore, the current literature may overestimate occurrences of roadkill removal by scavengers, while overlooking the role of road users in carcass removal.

Persistence time was not affected by road traffic or species' body mass, leading to a single estimate of roadkill persistence encompassing all tested species and roads, but extending this estimate to other species and other types of roads will require further work.
Roadkill rates are often under-estimated because a proportion of the roadkill disappears from the road before it can be counted. Studies have estimated the persistence of roadkill carcasses on roads and its relationship with factors such as species and road traffic. However, little attention has been given the different mechanisms of roadkill removal, how often they occur and how they affect the relationships between carcass persistence, road traffic and species. The overall spatial and specific patterns of roadkill persistence are the result of the contrasting patterns occurring within each source of removal, and we need to know how it is being removed in order to understand how fast it will disappear. We placed 86 fresh wild carcasses of birds and mammals on roads with varying volumes of traffic, and positioned a camera-trap on each of them to follow the disappearance of the carcasses. We were able to determine the source of removal in 61% of cases, and we find that people removed roadkill at least 30% of the time, and more often targeted large species on less frequented roads, while small carcasses placed on frequented roads were crushed by vehicles until they are no longer visible (28% of photographed removals). However, we only witnessed 3% of carcasses being removed by scavengers, which is less than what is assumed to occur during most roadkill persistence studies. Roadkill removal through the action of road users, often considered anecdotal in studies, can represent a significant proportion of the removals with people removing the largest species (previously thought to have the longest persistence) in an average of only 5 hours.
# 1. Introduction

The ever-expanding global road network encompasses all the major drivers of biodiversity loss identified by the IPBES (Díaz et al., 2019). While terrestrial transportation contributes to global greenhouse gases emissions (European Environment Agency, 2019), roadsides accumulate chemical pollutants such as heavy metals and salts, impacting the fauna and flora of the surrounding habitats (Seiler, 2001). In addition, roads and road traffic generate acoustic and light pollution, habitat destruction and fragmentation that disrupt ecological processes and are detrimental to the viability of many species (Forman & Alexander, 1998). Collisions between wildlife and terrestrial transports is a substantial source of mortality for many species (Moore et al., 2023) that amounts to an estimated 220 million birds and mammals in Europe annually (Grilo et al., 2020).

Roadkill data is often used as proxy to mortality, therefore serving as a basis to identify roadkill hot-spots (Girardet et al., 2015; Gomes et al., 2009; Teixeira et al., 2013) and to implement mitigation measures such as crossing structures or fences (Aresco, 2005; Rytwinski et al., 2016). Roadkill data is usually obtained from periodic surveys of animal carcasses along sampled roads. The raw number of detected carcasses is, however, an under-estimation of the real number of wildlife-vehicle collisions because of carcass detection and persistence (Teixeira et al., 2013). Carcass detectability is strongly influenced by the size of the animal (Barrientos et al., 2018), the position of the animal carcass on the road (Guinard et al., 2012) and the travel speed and level of experience of the observers (Collinson et al., 2014; Guinard et al., 2023; Langen et al., 2007; Paraguassu-Chaves et al., 2020). The biases emerging from undetected carcasses can be addressed by selecting the most appropriate survey methodology for the species, such as conducting the surveys on foot (Santos et al., 2016), lowering the travel speed of the observer (Collinson et al., 2014) or with ad-hoc data corrections using detection rate estimates (Gerow et al., 2010).

However, an animal can also be hit and then disappear from the road anytime between the collision and the survey, and in consequence, a portion of the mortality from vehicular traffic can never be counted during surveys (Guinard et al., 2012). Several studies have produced estimate of roadkill carcasses persistence times (Antworth et al., 2005; Henry et al., 2021; Ratton et al., 2014; Santos & Ascensão, 2019; Stewart, 1971). The most comprehensive study of roadkill persistence was published in 2011 by Santos *et al.*, who show that not only does roadkill remain on the road for short windows of time (usually a few days at most), but persistence time is also variable across areas, species, body mass, road traffic, weather, and season. Large-bodied species persist longer on roads (see also Teixeira et al., 2013), and the persistence of roadkill is negatively linked with the volume of traffic (see also Santos & Ascensão, 2019). Yet, these results are not always replicated, and the complexity of roadkill persistence on roads and its dependence on spatio-temporal predictors remains poorly understood (Bénard et al., 2023).
These results have substantial implications for roadkill monitoring: road surveys that are not conducted every few days likely miss an important part of the mortality (Teixeira et al., 2013), and roadkill monitoring schemes that cover multiple species, large temporal or large spatial scales will be inconsistently biased following the variations in carcass persistence (Santos et al., 2015). The mechanisms by which the carcasses disappear from roads remain the most likely drivers for the observed variations in roadkill persistence, e.g., road traffic volume influences the speed of disappearance when carcasses are repeatedly crushed by vehicle tires, or the season and geographical location impact the rates of roadkill consumption by carrion-feeders (Santos et al., 2011).

Despite several studies estimating carcass persistence on roads, little effort has been produced regarding these mechanisms of roadkill carcass disappearance. Roadkill carcasses can disappear through a limited number of mechanisms: the action of scavengers (Antworth et al., 2005), mechanical crushing of tissues from vehicle tires (Beckmann & Shine, 2015; Bénard et al., 2023), picked up or displaced by people (Abbate, 2019). Because roadkill carcasses seldom persist beyond a few days on roads (Santos et al., 2011), disappearances from the road surface and shoulder through decomposition by microorganisms may be marginal (Brand et al., 2003). A few studies have focused on the communities of scavengers that feed on roadkill with the help of sand to analyze footprints (Antworth et al., 2005; Beckmann & Shine, 2015; Slater, 2002), but the majority of authors deduce the mechanisms of removal at play *ad-hoc* based on the observed patterns of carcass persistence (Hubbard & Chalfoun, 2012; Ratton et al., 2014; Santos et al., 2011).

Because of this, it appears essential to (1) quantify the relative frequencies of carcass removal by scavengers, by people, and through the mechanical crushing of vehicle tires, (2) to explore the relationships between the source of removal and factors such as body mass and road traffic and (3) to explore the relationships between the persistence time of roadkill, its body mass, and the traffic volume for the three putative mechanisms. Existing hypotheses covering the relationships between the source of disappearance, traffic volume and body mass include: when smaller carcasses disappear faster, roadkill is likely removed by vehicles as small animals are probably more easily torn apart by tires (Santos et al., 2011), and high volumes of traffic deter scavengers, which would explain that roadkill usually has shorter persistence times on roads with less traffic (Ratton et al., 2014; Santos et al., 2016; Santos et al., 2011; Slater, 2002). However, if roadkill is crushed by vehicles until it disappears, then we would expect that higher volumes of traffic shorten the persistence of roadkill on roads (Stewart, 1971), and if scavengers are responsible for roadkill disappearance, we would expect that smaller animals have longer persistence times because (1) larger carcasses would be more visible to scavengers (Antworth et al., 2005) and (2) scavenging on roadkill is a dangerous activity for carrion feeders (Erritzoe et al., 2003) and
the risk-benefits balance for feeding on roadkill suggests that small-sized carcasses would not be
preferred (optimal foraging theory, Stephens et al., 2007; also suggested by Hastings et al., 2019).

In cases where roadkill is removed by people, which is seldom described in the literature, we would also expect the largest animals to have the shortest persistence, because they are the most visible to drivers (Barrientos et al., 2018). Predicting the persistence of roadkill that people remove in relation to traffic volume is less straightforward: on the one hand roads with high traffic offer an increased likelihood that a driver willing to removed roadkill carcasses will drive by, but at the same time smaller roads might offer more possibilities for stopping and safe parking in order to handle the carcass. It is likely that, in any given case, several sources are concurrently responsible for roadkill disappearance (Hubbard & Chalfoun, 2012), such that part of the roadkill is eaten by scavengers, part is crushed by vehicles and part is removed by people. The relationships between carcass body mass, road traffic and the persistence of roadkill on roads is therefore the outcome of multiple mechanisms that are poorly understood.

In this study, we aimed at identifying the sources of disappearance, measure persistence time of carcasses
by placing fresh carcasses on roads equipped with camera traps and assess the relationships between road
traffic volume, carcass' species' body mass and persistence. We predict that roadkill is removed by
several sources, and that body mass and the volume of traffic can predict the probability of disappearance
through each of the sources. In addition, we predict that carcass body mass and road traffic impact the
persistence time of roadkill in contrasting ways, depending on the way it was removed from the road
(Table 1).
<table><caption>Table 1: Predictions of the relationships between the possible sources of removal of roadkill carcasses from roads, the size of the carcass, and the volume of traffic of the roads. We make predictions for the probabilities of a carcass being either picked up or displaced by someone, being consumed by scavengers, or being crushed by the tires of passing vehicles until it is no longer visible. In addition, we make predictions for the persistence time (speed of disappearance) of carcasses under each possible source of removal. 1. Santos et al. 2011 – 2. Ratton et al., 2014; Santos et al., 2016; Santos et al., 2011; Slater, 2002 – 3. Stewart, 1971 – 4. Antworth et al., 2005.</caption><thead><tr><th></th><th>Source</th><th>Effect of carcass size</th><th>Effect of road traffic volume</th></tr></thead><tbody><tr><td rowspan="3">Frequency of removal mechanism</td><td>People</td><td>Positive - large animals disappear more often because of people than smaller ones because of increased detectability</td><td>Positive - more people driving by the carcass increase probability of removal or<br>Negative - easier to park a vehicle on a road with less traffic</td></tr><tr><td>Scavengers</td><td>Positive - large animals more detectable<sup>4</sup> and preferred by scavengers under optimal foraging theory</td><td>Negative - scavengers avoid roads with high traffic volumes<sup>2</sup></td></tr><tr><td>Crushed by tires</td><td>No prediction</td><td>Positive - high traffic deters scavengers and people, leaving carcasses to be crushed</td></tr><tr><td rowspan="3">Persistence time</td><td>People</td><td rowspan="2">Negative - the bigger the animal, the quicker it will get detected<sup>4</sup></td><td>No prediction</td></tr><tr><td>Scavengers</td><td>Negative - the less traffic, the more accessible to scavengers<sup>2</sup></td></tr><tr><td>Crushed by tires</td><td>Positive - dismemberment by tires is quicker for small carcasses<sup>1</sup></td><td>Negative - more vehicles means less time for carcass to be entirely crushed<sup>3</sup></td></tr></tbody></table>
## 2. Material and Methods

### 2.1 Study location and experimental design

We conducted all the experiments in the south of the Ain department, central-east France, between October and March. This area is dominated by agricultural (56.2%) and forested or semi natural (43.6%) landscapes (CORINE Land Cover, 2018) with a road density of 0.88 km per km² (fig. A1). Our area of study contained the Dombes, a mosaic of ponds and wetland (Ramsar, 2023) with high species richness (Bernard & Lebreton, 2007). We selected 15 roads in peri-urban or rural areas covering a range of traffic of 500 to 14 000 vehicles per day, representative of the diversity of traffic of the road network in south-east France (French Ministry of Ecological Transition, 2023). Very high traffic roads (>14 000 vehicles/day⁻¹) were not included due to safety reasons of the observers (AB). To our knowledge, police and road patrols (Direction Départementale des Territoires, Direction Interdépartementale des Routes) are not instructed by law to remove the roadkill they encounter.

We set up “Cuddeback 1279” camera-traps with a “Black Flash” module (night vision without a visible flash) at 31 locations on the 15 roads (fig. A1). We ensured that traps on the same road were located at least 2km from one from the other, and selected the most appropriate locations on the road based on accessibility and ability to hide the trap from people. We placed the traps on a tree on the verge of the road, such that the entire width of the road was in the field of view of the camera. We programmed the traps to take 3 photographs at each movement triggering the sensors, plus an additional time-lapse photo every 3 hours. This camera setting ensured that, should the trap not trigger as expected when the animal was removed from the road, we could estimate the persistence from the time-lapse with a minimal accuracy of 3h. Because the traps were difficult to set-up and the appropriate locations were limited, we used the same traps for consecutive experiments by placing new carcasses once the previous carcass had disappeared, alternating birds and mammals, as well as small and large species.

We obtained 79 bird and mammal carcasses from a wildlife rehabilitation center, stored at -20°C and thawed for 24h before the experiments. The 38 mammalian specimens ranged from brown rat (*Rattus norvegicus*, 300g) to roe deer (*Capreolus capreolus*, 25kg), and the 41 birds from blue tit (*Cyanistes caeruleus*, 20g) to Eurasian eagle-owl (*Bubo bubo*, 2.5kg). The complete list of specimens is made available in appendix A1. We retrieved the average body mass of each species from publicly accessible databases (Ocampo et al., 2021; Tobias et al., 2022). We placed the animals on the road surface in front of a camera trap, alternating between the center of the lane and the border between the road and shoulder. Note that we placed carcasses >5kg in weight (fox, badger, and roe deer) on the shoulder and not on the
lane due to safety concerns for drivers. We checked each location on foot every 1 to 3 days for camera-trap maintenance and noted whether the carcass was still present on each occasion.

## 2.2 Data analysis

We analyzed the photographs to estimate the time of carcass disappearance from the road (*photographed removal*), the interval of removal if the trap did not trigger (*interval disappearance*), or the moment the trap stopped functioning (*monitoring stop*). Note that the source of disappearance (scavenger, mechanical crushing of tissues by vehicles, active removal by people) could only be determined in the first case. We considered an animal ‘disappeared’ from the road once it could no longer be detected by a observer driving on the road (see Bénard et al., 2023, for a similar approach).

### Sources of disappearance

We classified three possible sources of disappearance for carcasses on the road: the mechanical action of vehicle tires, the removal by one or several people, and scavenger activity, plus cases with an unknown source of disappearance (e.g., if the trap failed during the experiment). Because of the low rate of roadkill consumption we observed (N=2), we did not include scavenging as a source of disappearance in the analyses. Therefore, each of the animal carcass had an assigned source of disappearance with no relevant alternative, and we used a multinomial logistic regression (Agresti, 2012) to determine the relationship between the 3 sources of disappearance (by people, by vehicles, undetermined), the body mass of species (continuous variable, based on the species' average body mass, see section 2.1) and the traffic of the road (continuous variable). We added the camera trap location as a random variable. This model analyzes the probability of each source of disappearance depending on the species' body mass and the traffic, which allows to test the hypotheses that some situations (namely, species and road traffic) are more conducive to some sources of removal (Table 1).

### Animal persistence on the road

We programmed the traps for time-lapse photos to obtain the persistence time of each carcass, even if a trap did not trigger during the removal. Consequently, some of the survival data is interval-censored, meaning the only information available is that the carcass disappeared sometime between the last
photograph and the next time-lapse photo. Ignoring interval-censoring in survival models will lead to biased survival estimates in either direction and confidence intervals biased downwards (Radke, 2003). Therefore, we used survival models that can incorporate interval-censoring to model the persistence time of the animals on the road (Lindsey & Ryan, 1998). We implemented Cox proportional hazards (thereafter: CoxPH) interval-censored survival models (Anderson-Bergman, 2017) to estimate the persistence time of the carcasses and to test the effects of body mass of species (continuous variable), traffic volume of the road (continuous variable) for all observations. In a second model, we tested the hypotheses summarized in Table 1 by specifying two-way interaction terms between the source of disappearance (three levels categorical variable: removed by people, removed by vehicles, undetermined disappearance) and body mass, and source of disappearance and road traffic. Survival models also allow to estimate the median survival time, which in our case is defined as the time at which 50% of the carcasses have disappeared from the road. Note that the notion of survival is translated differently when applied to roadkill persistence : the event of interest (“death”) is the disappearance of the animal from the road, and the time-to-event (“survival”) is the persistence of the carcass.

To our best knowledge, there is no implemented function for conducting mixed interval-censored
survival analysis in R, we therefore were not able to include the trap location as a random effect in the
survival models. We used packages *mclogit* (multinomial logistic regression, Elff, Martin, 2022) and
*icenReg* (survival models, Anderson-Bergman, 2017) for the R statistical software (R Core Team, 2021).

3. Results

We placed 79 animal carcasses in 31 locations equipped with camera traps. The median persistence on the road was 10h36 (95% CI: 5h52, 42h36). After 24 hours on the road, 34% of the animals were still visible. We could not obtain the source of disappearance for 31 animals: either traps did not trigger at the moment of removal, were stolen during the experiment, stopped working, or the animal carcass was pushed by the wind of passing vehicles out of the frame of the photographs.
## Sources of disappearance

We could not ascertain the source of disappearance for 39.1% of the animals: 13.5% were right-censored (either the animal was moved out of frame by vehicles: 5%; or the trap failed or was stolen: 8.8%) and in the remaining 25.3%, the trap did not trigger at the time of removal. Failure to determine the source of disappearance is explored in more detail in appendix 2. 30.4% of the animal carcasses were moved or picked up by people. In many cases of removal by people, the animal would be simply be placed in a ditch or hidden in the vegetation of the road shoulder (fig. A3). Finally, 27.8% were removed by the mechanical action of vehicles. Only 2 scavenger removals (2.5%) were evidenced: a European badger (*Meles meles*) removed a common swift (*Apus apus*) from a road of medium frequentation (3 180 vehicles.day⁻¹); and a red fox (*Vulpes vulpes*) and common buzzards (*Buteo buteo*) consumed the carcass of a European rabbit (*Oryctolagus cuniculus*) from a small road (931 vehicles.day⁻¹). We did not record / capture other instances of scavengers feeding on carcasses.

Using the removal by mechanical crushing as a baseline, a 1kg-increase of the body mass of the carcass' species increased the odds of being removed by people (Odd ratio (OR) = (95% CI: 1.12;1.38), z=-2.42, p=0.016), but did not change the odds of the removal source being undetermined (OR: 0.91 (95% CI: 0.82, 1.01), z=-0.89, p=0.38). A 100-vehicles increase in the daily road traffic volume decreased the odds of a removal by someone (OR = 0.96 (95% CI: 0.95;0.98), z=-2.42, p=0.016) and did not change the odds of an undetermined disappearance (OR: 0.99 (95% CI: 0.98, 1.005), z=-0.428, p=0.67; fig. 2). Using undetermined disappearance as a baseline, a 1kg-increase of the body mass increased the odds of the carcass being removed by someone (OR = 1.36 (95% CI: 1.22;1.51), z=2.91, p=0.003, fig. 1). A 100-vehicles increase in the daily road traffic volume decreased the odds of removal by someone (OR = 0.96 (95% CI: 0.95;0.98), z=-2.22, p=0.03).

In summary, larger animals and smaller roads were associated with increased odds of removal by people. Conversely, bigger roads were associated with increased odds of either removal by vehicles or a removal mechanism we were not able to ascertain. This is in support of our hypotheses that: people remove animals more often when the animal is bigger (positive link between body mass of the species and odds of human removal), and the probability of carcass disappearance through mechanical crushing is (1) negatively linked with body mass and (2) positively linked with the volume of traffic of the road (Table 1).
<figure><img src="image_43.png" /><figcaption>Fig. 1: We placed animal carcasses on roads in south-east France and quantified the sources of disappearance: removed or displaced by people (top) or crushed by vehicles until it was no longer visible (middle). Scavenging was rare (2.5% of cases) and is not represented here. In a number of cases, we were unable to determine the source of removal (bottom). The prediction for probability of occurrence of each source of disappearance in relation to body mass of the species placed on the road are represented (grey: 95% CI), along with the data points at the bottom of each graph.</figcaption></figure>
Predicted probability of each source of carcass disappearance

<figure><img src="image_44.png" /><figcaption>Fig. 2: We placed animal carcasses on roads in south-east France and quantified the sources of disappearance: removed or displaced by people (top) or crushed by vehicles until it was no longer visible (middle). Scavenging was rare (2.5% of cases) and is not represented here. In a number of cases, we were unable to determine the source of removal (bottom). The prediction for probability of occurrence of each source of disappearance in relation to the traffic of the road are represented (grey: 95% CI), along with the data points at the bottom of each graph.</figcaption></figure>
Animal persistence on the road

We obtained the exact time of removal for 38% of the animal carcasses, a time of removal contained within a known interval for 40% of the carcasses and the remaining 22% were right-censored either due to the trap failure, theft, or the animal being moved out of the frame of the photographs. The median persistence of animals removed by people was 5h27 (95% CI: 2h16, 23h42), while animals removed by vehicles persisted on average 20h28 (95% CI: 10h40, 83h) and undetermined removal cases persisted for 20h52 (95% CI: 4h43, 411h30, fig. 3). Note that the upper limit of the confidence interval for the undetermined disappearances is very high, a consequence of the high proportion of these observations being right-censored.

<figure><img src="image_45.png" /><figcaption>Fig. 3: Kaplan-Meier survival curves of the persistence of roadkill carcasses on the road. We identified three sources of removal for roadkill: scavengers eating the carcass, people removed the carcass and vehicle crushing the carcass until complete removal, plus 33 cases where we could not determine the source of disappearance. Scavenger-related removal is not shown here as there was only 2 occurrences. The median persistence time (time for 50% of carcasses to be removed, red line) is shorter for animals removed by people (about 5h30) than by vehicles (about 20h30) and undetermined cases (about 21h). These differences were not found to be statistically significant. The 95% confidence interval of the Kaplan-Meier curves are not represented here for clarity.</figcaption></figure>
There was no effect of the species' body mass (Hazard Ratio=0.997 (95% CI: 0.991,1.0), z=-0.68, p=0.49) nor the volume of traffic of the road (Hazard Ratio=1.001 (95% CI: 0.96,1.04), z=0.028, p=0.97) on the persistence of carcasses on the road.

Including the source of disappearance in two-way interaction terms, the second interval-censored CoxPH model (fig. 4) showed no significant differences in the hazard ratios (HR) of removals by people vs. vehicles (HR=1.60 (95% CI: 0.83, 3.06), z=0.72, p=0.47), undetermined vs. vehicles (HR=1.36 (95% CI: 0.66, 2.81), z=0.43, p=0.66) or undetermined vs. people (HR=0.85 (95% CI: 0.36,2.01), z=-0.18, p=0.85). In other words, expected persistence times for roadkill are not statistically different between each source of disappearance.

A 1kg-increase in the carcass' species body mass had a tendency to increase the persistence of animals removed by vehicles (HR=0.75 (95% CI: 0.48,1.17), z=0.72, p=0.47, reference level: *people*) and decreased the persistence when removed by people (HR=1.46 (95% CI: 0.93,2.29), z=1.66, p=0.096, reference level: *vehicle*) but these trends are not statistically significant. Only small sized species disappeared faster than larger ones in cases where the disappearance was not witnessed, using cases where the carcass was removed by people as the reference level (HR=0.25 (95% CI: 0.10, 0.64), z=0.72, p=0.02). An increase in road traffic (unit of increase: 100 vehicles.day⁻¹) was not associated to the persistence time of animals, whatever the source of removal we considered (people removal: HR=0.99 (95% CI: 0.96,1.01), z=-1.29, p=0.20, reference level: *vehicle*; vehicle removal: HR=1.01 (95% CI: 1.001,1.02), z=1.26, p=0.2, reference level: *people*; undetermined removal: HR=1.01 (95% CI: 0.98,1.03), z=1.03, p=0.30, reference level: *people*).

In summary, we found no statistical support for the predicted relations between the volume of traffic, the source of disappearance and the time during which the animal persisted on the road, nor between the body mass of the species, the source of disappearance and the persistence (Table 1).
Hazard ratio (95% CI) of the disappearance
from road

A. carcass persistence depending on the source of disappearance

<figure><img src="image_46.png" /></figure>

B. relations between continuous predictors and persistence

<figure><img src="image_47.png" /></figure>

Fig. 4: Effects of road traffic volume (vehicles.day⁻¹) and carcass' species body mass (kg) on the persistence on the road in south-east France, tested between October and March. Interpretation of hazard ratios in the context of roadkill persistence is as follows: A. When comparing different sources of disappearance (removed by people, removed by the mechanical crushing of vehicle tires, undetermined source of disappearance), a hazard ratio (HR) > 1 means a shorter persistence on the road for the first category, and conversely for HR < 1. B. For continuous predictors, an increase of 1kg in the carcass' species body mass shortens the persistence if HR > 1, and increased the persistence HR < 1 (no impact if HR = 1). The same is true for an 100-vehicle increase in the daily volume of traffic of the road where the carcass was located.
Discussion

In this experiment, we document the mechanisms of disappearance of carcasses from roads, witnessing the disappearance of the carcass 60% of the time. Half of these carcasses were removed or displaced by people, and half were crushed by vehicle tires until they became invisible. Only 2 carcasses (2.5%) were consumed by scavengers. Larger carcasses and less frequented roads were more conducive to people removing the animals, in which case roadkill persisted on average about 5 hours. Smaller carcasses and bigger roads were more conducive to tires crushing the animals, or the source of disappearance being undetermined (average persistence time in both cases: 20 hours). The speed of disappearance, or persistence time, was not impacted by the volume of traffic or the species' body mass of the carcass both in general and when considering each source of disappearance separately.

The median persistence time for roadkill carcasses is generally estimated at 1 day, as few studies conduct experiments with a temporal resolution of less than 24 hours (Ratton et al., 2014; Santos et al., 2011; Slater, 2002). Through continuous monitoring with camera-traps, we refined this estimate to 10 hours on average. Short persistence times raise concerns for the interpretation of roadkill data collection, as monitoring scheme with a temporal resolution of >1 day are likely to miss an important proportion of the roadkill (Henry et al., 2021). In this study, 58% of the carcass had disappeared after 12 hours, and 66% after the first day, which implies that even daily roadkill surveys miss a significant part of the mortality on roads. However, increased frequencies in roadkill monitoring imply increased costs, and such expectations might not be realistic (Henry et al., 2021). In recent years, ad-hoc methods for roadkill data correction have emerged in response to these issues, which require robust estimates of persistence and thus a good understanding of the persistence of roadkill (Teixeira et al., 2013): using the results of this study, the correction factor (characteristic time $T_R$) for roadkill persistence, which we find to be constant across species and roads, is $T_R=40h36$ (95% CI: 10h37, 90h27).

Most of the studies estimating the persistence time of roadkill center around the idea that animals are removed by scavengers or by vehicle tires, and often construct experiments or hypotheses accordingly (Guinard et al., 2023; Ratton et al., 2014, 2014; Santos et al., 2011). In fact, roadkill removal by scavengers has been evidenced before using continuous observation and sand trays (Slater, 2002), yet we have observed few scavengers feeding on roadkill in this experiment. This is unexpected as we carried out most experiments during late fall, winter and early spring (October to early March), which is known to be a time of the year during which scavenging rates increase, as opportunistic scavengers switch to carrion-feeding when other food resources become scarce (Prosser et al., 2008; Selva et al., 2005). Others have suggested however that season is a poor proxy for scavenger activity levels (Barrientos et al., 2018; Ponce et al., 2010). In addition, we used the carcasses of wild individuals, which are thought to be more
attractive to scavengers than domestic species (Prosser et al., 2008) and conducted most of the
observations in a rural area with high biodiversity in which one would expect a rich community of
scavengers (carrion crow *Corvus corone*, Eurasian magpie *Pica pica*, red fox *Vulpes vulpes*, Schwartz et
al., 2018).

We have been unable to ascertain the source of removal for a third of the observations, so part of these
observations could be the result of scavenging, and we have also artificially placed carcasses on roads
which mean they likely did not follow hotspots of roadkill presence expected by scavengers in the area
(Santos et al., 2011). Interestingly, Winton et al. (2018) monitored snake roadkill consumption by
scavengers with camera traps and reported undermined cases similar to this study, where the snake would
be present on one photograph and absent on the next. Considering they had mostly recorded terrestrial
scavengers, they theorized that undetermined cases could be the result of avian scavengers dropping on
the road and flying off with the carcass. However, in this case we recorded very few scavengers,
mammalian and avian alike. We still obtained similar persistence estimates compared to previous work
(Ratton et al., 2014; Santos et al., 2011), suggesting the role played by scavenger activity in roadkill
persistence may not be as important as previously thought and that the removal of roadkill through tires
and people could be responsible for the observed persistence of roadkill in south-east France.

In this study, we have shown that roadkill may disappear from the road through different mechanisms: removed by people, removed by the action of vehicle tires, and in a few cases, scavenging. The frequencies of each of these removals is, however, markedly dependent on the size of the animal, which means the source of disappearance from the road is likely species-dependent. Roadkill removal by vehicles followed similar patterns to disappearances from undetermined sources: it concerned mostly smaller animals, and roadkill persistence time decreased with animal body mass (significant only for undetermined cases) with a median persistence time on the road of about 20 hours in both cases. As discussed in appendix A2, failure to determine the source of disappearance could be explained by different types of issues (no trigger, trap theft...), all of which were equally probable across all road traffic volumes and species' body mass (fig. A2).

In particular, traps not triggering could be the result of (1) animals moved out of frame by vehicles that did not trigger the camera trap sensors (not all vehicles triggered the traps, AB's personal obs.), such that the animal was visible on one photograph and not on the next, (2) vehicles crushing the animal until it disappeared, and none of these vehicles triggered the sensors, (3) scavengers feeding entirely on the animal without triggering the sensors and finally (4) people removing the animal without triggering the traps. The lack of association between non-trigger events and the species' body mass and road suggests that it is unlikely these events were specifically associated to one type of removal, except for scavenger
activity for which we have no knowledge of the facilitating factors. We lack the ability to give a
definitive explanation on this subject, although it should be noted that camera traps' ability to trigger can
be dependent on the temperature, battery level, and could be reduced when the subjects are moving too
fast (Marcus Rowcliffe et al., 2011). In particular, vehicles moved on the road at high speeds and did not
always trigger the traps.

Small fauna on roads was often removed by the action of vehicle tires, which can lead to very short persistence times (Stewart, 1971). In addition to short persistence times, small-bodied animals are more difficult to detect during surveys (Barrientos et al., 2018; Ogletree & Mead, 2020; Santos et al., 2016) which would make them the most under-estimated species in roadkill datasets. As we expected, the probability of disappearance through repeated crushing by vehicles was higher on more frequented roads, possibly because few people and scavengers will approach carcasses when there are too many vehicles (Erritzoe et al., 2003), leaving time for vehicles to crush the carcasses repeatedly. In addition, it is possible that a carcass that has been damaged by a vehicle (even though it is still very visible), which may happen quickly when the flow of traffic is high, may discourage members of the public from displacing it. However, our results did not show that road traffic influenced the speed at which the animal disappeared, due to more vehicles crushing them (Stewart, 1971). Although we did not include highways in our experiment as Stewart (1971) did, our experiment covered a wide range of traffic from 500 to 14 000 vehicles.day⁻¹ (mean: 4862 vehicles.day⁻¹) such that volumes of traffic that were too low are an unlikely reason for the lack of effect observed here (fig. 4). Carcass persistence being significantly shorter on highly frequented roads was previously evidenced for amphibian roadkill, but small passerines disappearance remained unaffected by the volume of traffic (Bénard et al., 2023). In this experiment, nearly all small passerines (80%) disappeared from all roads in 2 hours or less. It is possible that we lack a proper understanding of the mechanisms behind carcass removal by vehicles, and the importance of species characteristics, such as size, morphology, or bone density.

We found high rates of removal by people, especially for large carcasses, likely owed to their visibility
for drivers (Barrientos et al., 2018). Drivers were more likely to remove carcasses on smaller roads,
although the traffic of the road did not in itself affect the persistence of the animal. This could be the
result of the need for stopping and parking on roads to displace the carcass, easier to do when traffic is
low, and raises the question of fine temporal variations of roadkill removal by people as traffic volumes
also tend to vary throughout the day (Van Langevelde & Jaarsma, 2005). Carcasses removed by people
were also the fastest to be removed, persisting an average of only 5 hours on the road. In addition, when
removed by people, bigger carcasses had a tendency to disappear even faster. This is in direct
contradiction with the findings of Santos et al. (2011) where smaller animals disappeared faster, and the
heaviest animals (“carnivores”; Santos et al., 2011) had a median persistence time of 9 days. Their study
however did not include animals over 10 kg, when we had animals as heavy as 25kg. It is possible that
removals rates are area-dependent, and/or that their sample did not include animals large enough to
warrant being removed by people, either because roadkill for these species are not frequent in the area
they studied, or because large animals were killed and removed from roads fast enough to never be
counted during their daily road surveys.

The value of roadkill as an ethical source of meat for human consumption has been a subject of debate in recent years (Abbate, 2019; Wright, 2019), and some have suggested displacing carcasses is an act of respect or mourning (first author's personal communications, and see Calarco, 2023; Kopnina & Shoreman-Ouimet, 2013; Lunney, 2013). Carcasses left on the road also pose the threat of secondary collisions when attracting scavengers (Klöcker et al., 2006; Vidal-Vallés et al., 2018), which could drive people to want to remove them. Despite these possible explanations for roadkill removal by people, we lack a good understanding of how widespread this behaviour is, or how it is affected by culture or local laws. As demonstrated in this study, it can be a frequent occurrence that has a significant impact on roadkill persistence times and patterns, and thus warrants more interest from the scientific community.

Our results shed a new light on the complexities of roadkill persistence on roads. Because different
sources of removal affect the relations between animal body mass and persistence, it is possible that
overall relationships between persistence time and body mass are non-linear, with large and small
animals being removed more quickly (e.g., Guinard et al., 2012). Our results challenge the common
perception that a significant part of roadkill removal is the result of scavenging activity (Antworth et al.,
2005; Guinard et al., 2012; Schwartz et al., 2018), and explore the previously ignored impact of members
of the public displacing and removing roadkill, making it invisible to observers during roadkill surveys.
People removing roadkill impacts mostly larger animals, previously thought to stay the longest on roads,
and their removal of roadkill appears faster than the action of vehicles.
## Ethical statement

In compliance with the General Data Protection Regulation (GDPR) guidelines, articles 6 and 89, photographs of people captured in public spaces constitute personal data that was necessary to carry out the experiments presented in this paper. We have collected no other identifying information. Photographs showing faces, vehicle plates and other identifying information will not be shared.

## Acknowledgments

We would like to thank the Syndicat de la rivière d'Ain Aval et de ses affluents (SR3A) and the Ligue de Protection des Oiseaux (LPO) Auvergne-Rhône-Alpes for their help in conducting the experiments. We also thank the Tichodrome wildlife rehabilitation center for supplying animal carcasses.

## Funding

This work was supported by French Ministry of Research and Higher Education funding (PhD grant 2020-2023) and the SR3A.
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<figure><img src="image_48.png" /><figcaption>Fig. A1: Map of the area in which we conducted the carcass persistence experiments between October and March 2022 and 2023. We used Corine Land Cover data and nomenclature to characterize the study area (Corine Land Cover, 2018). Each red point represents an animal carcass we placed on the road surface or shoulder, with an infrared camera trap installed in the road side vegetation such that the animal is visible on the photographs. We used a total of 26 species donated by a wildlife rehabilitation center:</figcaption></figure>
Mammals

1 Least weasel (Mustela nivalis)

3 European badger (Meles meles)

4 Roe deer (Capreolus capreolus)

4 Red squirrel (Sciurus vulgaris)

11 European hedgehog (Erinaceus europaeus)

2 European rabbit (Oryctolagus cuniculus)

4 Pine marten (Martes martes)

1 Coypu (Myocastor coypus)

1 Brown rat (Rattus norvegicus)

7 Red fox (Vulpes vulpes)

Birds

1 swift (Apodidae spp.)

7 Common buzzard (Buteo buteo)

1 Common kestrel (Falco tinnunculus)

1 Carrion crow (Corvus corone)

5 European starling (Sturnus vulgaris)

1 Common pheasant (Phasianus colchicus)

1 Eurasian jay (Garrulus glandarius)

2 Great horned owl (Bubo bubo)

7 Common blackbird (Turdus merula)

1 Long-eared owl (Asio otus)

2 Passerine (Passeriformes spp.)

1 Green woodpecker (Picus viridis)

1 Great spotted woodpecker (Dendrocopos major)

2 Eurasian magpie (Pica pica)

4 Common pigeon (Columba livia domestica)

4 Common wood pigeon (Columba palumbus)
<figure><img src="image_49.png" /><figcaption>Fig. A2: We placed animal carcasses on roads equipped with camera-traps. In some cases, the source of disappearance of the carcass could not be ascertained because the trap failed or had been stolen, the trap did not trigger at the moment of removal, or the animal was displaced out of frame by passing vehicles. To explore possible biases in our results due to traps not triggering, we represent here the distribution of carcass body mass in each category compared to distribution in cases with known removal source (top) and the distribution of road traffic volumes (bottom).</figcaption></figure>

A multinomial logistic regression including the trap's position as a random effect showed that the species' body mass and the traffic volumes had no significant influence on the type of issue we faced. Nonetheless, animals displaced out of frame by vehicles appear to be mostly smaller animals, meaning that particular care should be given to the positioning of the traps with small species as to photograph a wide area. Note that traps not triggering can be due to a variety of reasons, such as low battery and weather conditions (which can affect the sensors' sensitivity, Rowcliffe et al., 2011).
<figure><img src="image_50.png" /><figcaption>Fig. A3: Examples of roadkill removal evidenced with camera-traps.</figcaption></figure>

Top left: European rabbit removal by scavengers.

Top right: Adult roe deer (intact carcass) removed by someone and placed in the forest 5 meters away from the road. The animal was not visible by drivers.

Bottom: Red fox progressive disappearance from mechanical crushing by vehicle tires. The moment of removal is defined as the moment when the species is no longer recognizable.
# Case study: informing roadkill persistence time with citizen science

## CONTEXT

Roadkill persistence is an essential factor for accurate study of wildlife-vehicle collisions. Research has shown that various spatio-temporal factors impact this persistence, making it challenging to extend existing persistence estimates to new roadkill monitoring projects. However, citizen science initiatives, which currently enable the monitoring of roadkill across broad spatio-temporal ranges at a fraction of the effort required for professional surveys, may also be a promising avenue for estimating roadkill persistence in new contexts.

## OBJECTIVES

- Formulate a methodology to assess the persistence of roadkill, relying on citizen sciences.

- Estimate persistence, along with the impacts of two previously investigated predictors – species' body mass and road traffic volume – and a newly introduced predictor: season.

## RESULTS

This preliminary experiment achieved reasonable spatio-temporal scales, yielding results that demonstrated a notable impact of traffic volume on persistence. This outcome, which differs from previous results, could be attributed to the inclusion of highways as a novel road type in the dataset. Furthermore, the persistence estimates obtained through this methodology aligned with previous findings, and no effect of the season on roadkill persistence is evidenced.

## CONCLUSIONS

Citizen science proves a viable approach for estimating roadkill persistence and investigating its spatial, temporal, and biological predictors. This cost-effective method has enabled us to expand the scope of our experiments, both in terms of time and locations, which has led to new insights.

The straightforward nature of this experimental design makes it well-suited for implementation alongside existing citizen science roadkill monitoring programs or as stand-alone research projects.
Introduction

In the two previous manuscripts, we worked toward estimates of the persistence of roadkill on roads by
(1) placing fresh carcasses in front of camera traps, and (2) by following the disappearance of carcasses
at intervals of 2 hours. We encompassed a wide range of species' sizes, from great tit *Pajus major* to roe
deer *Capreolus capreolus*, and of road traffic volumes, from 500 to over 15 000 vehicles.day⁻¹. In both of
these studies, we found generally short persistence times for all species, with most of the roadkill
disappearing in less than 24 hours. We also found evidence of a spatial distribution in the persistence of
amphibians, as persistence was strongly influenced by the volume of traffic of the road. In addition, in
the previous manuscript, road users were seen removing a third of the carcasses we placed on the road,
after an average of 6h (95% CI: 2h,24h), which was much shorter than disappearance through
mechanical crushing (about 20h) and other average persistence estimates found in the literature (see next
paragraph). There might be a spatial component to the removal of carcasses by people, as we do not
know yet if this habit is widespread or specific to certain areas and cultures (other documented evidence
of this behaviour: Calarco, 2023; Kopnina & Shoreman-Ouimet, 2013). We have lightly touched on
temporal variations (rainfall) in roadkill persistence, but have yet to explore seasonal effects, which have
previously been demonstrated in Portugal (Santos et al., 2011)

Another indication of the possible presence of a spatio-temporal structure in roadkill persistence is the
disparity in the persistence estimates presented in published studies. We found an overall median
persistence time of 10 hours (95% CI: 5h, 42h) for a wide range of mammal and bird species and sizes of
roads (previous manuscript). Santos et al. (2011) found substantially longer median persistence times
than we did for hedgehogs (4.5 [3,7] days), birds of prey<fnref n="5" /> (6 [4,9] days) and mammalian carnivores (9
[5,19] days). Henry et al. (2021) found a median persistence of 8 (0,17) days for large mammals<fnref n="6" />, and 2
(0,4) days for passerines and small mammals<fnref n="6" />. Ruiz-Capillas et al. (2015) found an average persistence
of 6.65 (6.24,7.06) days for small rodents. Conversely, Stewart (1971) found that house sparrows
(Passer domesticus) carcasses deposited on an interstate highway persisted on average less than 90
minutes, and chick carcasses placed by Ratton et al. (2014) on highways and dirt roads persisted an
average of 12h. The persistence of roadkill, useful to calibrate survey methodologies, appears highly
variable depending on the species but also presumably on locations, weather, or the distribution of road
traffics.

<footnote n="5">5 Birds of prey: diurnal and nocturnal; accipitriforms and strigiforms (Santos et al., 2011)</footnote>

<footnote n="6">6 Large mammals: 4.5 to 40 kg; Passerines: all passerine species; Small mammals: < 70g (Henry et al., 2021)</footnote>
## The value of citizen sciences for estimating roadkill persistence

Consequently, estimates of roadkill persistence that aim at correcting a roadkill dataset should ideally be obtained in the context of the roadkill monitoring scheme, in order to match the geographical, cultural and ecological parameters of the area where the roadkill data is collected. Methods for estimating persistence can be implemented concurrently to the regular monitoring of roads with capture-recapture approaches, which means leaving the carcasses on the road during surveys and noting the presence or absence of each carcass during each consecutive survey (see Guinard et al., 2012 for an application of this methodology). However, in the context of roadkill monitoring with citizen sciences, this approach is not as straightforward: citizen science roadkill monitoring schemes usually continuously monitor roadkill within a wide geographical area (Vercayie & Herremans, 2015).

Two options are possible for roadkill counts correction in the context of citizen science: estimating the persistence of roadkill at a smaller scale (i.e., a limited geographical area and period) and upscale these estimates at the larger scale of the monitoring project (as we have attempted in sections 1 and 2 of this chapter), or estimating persistence at a much larger scale by relying on the project contributors themselves, who can cover spatial and temporal ranges comparable to the monitoring project they take part in. In other words, roadkill persistence experiments have the potential to be carried out through citizen science, serving as a supplementary activity for participants alongside their usual roadkill reporting duties. In this case study, we explore the feasibility of roadkill persistence monitoring using Faune-AuRA contributors and compare the results obtained with this methodology to the two previous manuscripts dealing with roadkill persistence as well as published results.

# Methods

## Volunteer contributors recruitment

We recruited volunteer contributors with announcements through the LPO communication channels (many LPO members also take part in the Faune-AuRA monitoring), as well as via a local newspaper<fnref n="7" />. Volunteers provided us with a set of roads they accepted to cover by car at least daily, either because it

<footnote n="7">7 “Des volontaires recherchés pour évaluer la mortalité des animaux sur les routes de l’Ain” – 05/05/2022, Bugey Côtière [url]</footnote>
was their daily itinerary to and from work, or because they lived in the area. They were tasked with
monitoring roadkill carcasses present in their area at least every 24h: taking a picture and sending it to
the author's phone number, along with a comment on the visibility of the animal from the road (whether
it was visible to drivers or not). The volunteers could either detect a fresh carcass themselves, or be asked
to monitor a carcass that had been reported in Faune-AuRA by another contributor. For this second
option, we monitored the Faune-AuRA database bi-daily to signal new roadkill reports.

The recruitment of volunteers required the disclosure of personal information such as names, contact, and
places of residence. Particular care was given to inform participants of their rights under the GDPR
(General Data Protection Regulation) guidelines during this process.

Statistical analyses

We obtained daily observations of the presence or absence of roadkill carcasses, along with the species'
body mass (continuous variable, obtained from manuscript n°3), the daily average traffic of the road
(continuous variable, obtained from departmental councils and the Ministry of Ecological Transition,
2019) and the season (spring, summer and winter). This type of persistence data is very similar to data
obtained by Santos et al. (2011), and we analyzed the data following the same methodology. We
estimated the median roadkill persistence using a Kaplan-Meier estimator, and constructed a mixed semi-
parametric survival model (Cox-Ph model) to analyze the persistence probability of the carcasses in
relation to the road traffic (continuous, see previous manuscript), season (categorical) and carcass'
species body mass (continuous, see previous manuscript) with the ID of the observer as a random effect.
In addition, we grouped the roadkill species into categories based on animal size and taxon and computed
the survival curves for each group, to enable a comparison with Henry et al. (2021) and Santos et al.
(2011) who presented their results in a similar manner. We used the packages *survival* (Therneau &
Grambsch, 2000) and *coxme* (Therneau, 2022) for R (R Core Team, 2021).
# Results

## Volunteers turnout

We recruited a total of 12 volunteers who agreed to cover a total of 2886 km² of the AuRA region (4.14%), which amounted to 6.04% of the road network (fig. 1). In total, we obtained 55 observations of roadkill persistence covering the spring, summer, and winter of 2021. Volunteers were generally motivated to conduct the observations, although they could not always follow carcasses every day as required, which conducted 8 (14.5%) of the observations to be right-censored. As a side note, in another 14.5% of the observations, volunteers reported that the intact carcasses had been moved into the shoulder vegetation or in a ditch, making it likely the removal was the result of the intervention of someone.

Monitored roads covered a range of 400 to 14 044 vehicles.day⁻¹, with the notable inclusion of two highways (A6 and A89). We obtained observations for 19 species from 0.015 to 25kg. The observed species were: roe deer (*Capreolus capreolus*), European badger (*Meles meles*), red fox (*Vulpes vulpes*), coypu (*Myocastor coypus*), European hare (*Lepus europaeus*), pine marten (*Martes martes*), European rabbit (*Oryctolagus cuniculus*), red squirrel (*Sciurus vulgaris*), brown rat (*Rattus norvegicus*), common buzzard (*Buteo buteo*), tawny owl (*Strix aluco*), domestic pigeon (*Columba livia*), Eurasian chaffinch (*Fringilla coelebs*), common blackbird (*Turdus merula*) and European robin (*Erithacus rubecula*).
<figure><img src="image_51.png" /><figcaption>Fig. 1: Map of the Auvergne-Rhône-Alpes region with the area covered by the volunteers in the roadkill persistence experiment. Each volunteer provided an area in which they accepted to follow the disappearance of roadkill carcasses at least daily. Total coverage by volunteers represented about 4% of the region surface.</figcaption></figure>

## Persistence estimates

The median persistence time was estimated at 43h (95% CI: 24,62). The longest persistence was observed for small mammals (pine marten, red squirrel and brown rat) at 59h (95% CI: 24,NA) and the shortest median persistence was observed for non-birds of prey (23h (95% CI: 21.2,NA), common blackbird, European robin, domestic pigeon, Eurasian chaffinch, fig. 2). Note that ‘NA’ estimations with Kaplan-Meier estimators are a result of right-censoring (Goel et al., 2010).

The Cox-PH model showed no significant effect of the species' body mass (Hazard ratio (HR)=0.979 (95% CI: 0.95,1.001), z=-0.70, p=0.48) or season (summer vs. spring: HR= 0.52 (95% CI: 0.29,0.91), z=-1.15, p=0.25; winter vs. spring: HR= 0.56 (95% CI: 0.37,0.84), z=-1.43, p=0.15). Increasing the road traffic volume increased the persistence of the carcasses (HR=0.93 (95% CI: 0.89,0.96), z=-2.20, p=0.03).
<figure><img src="image_52.png" /><figcaption>Fig. 2: Survival curves of roadkill persistence on the road.</figcaption></figure>

Birds: domestic pigeon (Columba livia), Eurasian chaffinch (Fringilla coelebs), common blackbird (Turdus merula) and European robin (Erithacus rubecula), median persistence: 23h (95% CI: 21.2,NA);

Birds of prey: common buzzard (Buteo buteo), tawny owl (Strix aluco), median persistence: 48.1h (95% CI: 21.6,NA);

Large mammals: roe deer (Capreolus capreolus), European badger (Meles meles), red fox (Vulpes vulpes), median persistence: 43.5h (95% CI: 24,NA);

Medium-sized mammals: coypu (Myocastor coypus), European hare (Lepus europaeus), pine marten (Martes martes), European rabbit (Oryctolagus cuniculus), median persistence: 36 (95% CI: 24,NA);

Small mammals: red squirrel (Sciurus vulgaris), brown rat (Rattus norvegicus), median persistence: 59 (95% CI: 24,NA).
# Discussion: is citizen science a viable method for estimating roadkill persistence?

In this short case study, we demonstrated a methodology for obtaining persistence estimates at large spatial scales using citizen science. We were successful in obtaining 55 reports of carcass persistence, covering a range of road traffic and species comparable to our previous experiments, but covering much larger geographical and temporal ranges. We estimated median persistence times of about 2 days across all species, which is comparable to published estimates (Henry et al., 2021; Ruiz-Capillas et al., 2015; Santos et al., 2011). However, while Santos et al. (2011) reported that small mammals and small birds disappeared faster than birds of prey, we found the reverse. We found no effect of the species' body mass on persistence, which is again in contradiction with Santos et al. (2011) and Henry et al. (2021), but is consistent with the conclusions of our previous manuscript. However, contrary to our previous findings, we found a significant effect of traffic volume on roadkill persistence, with roadkill persisting longer on more frequented roads (similar conclusions in Santos et al., 2011).

The volunteers sometimes reported that the animal had been placed in a ditch or in the vegetation of the road shoulder but had remained intact (14.5% of cases), which suggests that the roadkill removal by road users evidenced in the previous manuscript (limited to the Ain department) is also observed in other departments of the AuRA region (Isère, Loire, and Savoie departments). While season influenced roadkill persistence in other studies (Guinard et al., 2012; Santos et al., 2011), we did not find significant differences in roadkill persistence between summer, spring, and winter. It is often thought that seasonal differences in persistence are driven by scavenger presence and activity (Prosser et al., 2008; Santos et al., 2011), but we have uncovered little evidence of roadkill consumption by scavengers in the AuRA region (or at least the Ain department) in the previous manuscript.

The median persistence time of roadkill we estimated previously is lower than the results obtained in this experiment, with an overlap of the 95% confidence intervals (manuscript n°3: 10h36 (95% CI: 5h52,42h36); citizen science approach: 43h (95% CI: 24h,62h), fig. 3). However, there was no overlap between the confidence interval of the Kaplan-Meier survival curves for the first 24 hours, and we found a significative effect of the road traffic. The citizen science approach covered a larger area than we managed with more traditional experimental designs, with volunteers located in several departments of the region AuRA while we only conducted the camera-trap experiments in the south-east area of the Ain department. It is possible that roadkill persistence at the scale of the region is longer than what we observed in the Ain department.
There were also differences in methodology between the two studies: the *camera-trap* experiment (previous manuscript) involved placing animal carcasses on the road while the *citizen science* approach presented here only targeted roadkill that was either (1) detected by the volunteers or (2) reported by another contributor of the Faune-AuRA project. In consequence, there is a probable bias for animals with higher detectability and with longer persistence in the *citizen science* sample. Animals that disappear very quickly are less at risk of being selected for the citizen science approach, because the probability of being reported by a contributor was lower. In fact, some of the species placed on the road in the *camera-trap* experiment disappeared in a few minutes only, usually when they were located in the path of the vehicle's tires. This may also be the case in published studies of roadkill persistence which employ a capture-recapture method, as daily capture-recapture surveys may overlook the carcasses with very short persistence times (i.e., a few hours or less). The heterogeneity in capture probability may result in positively biased estimates of persistence (Pledger et al., 2003).

In addition, this study managed to cover two highways of the region, which we did not include in the
*camera-trap* experiment due to safety concerns. In this instance, removing the data collected on
highways from the Kaplan-Meier estimates and Cox-PH model lowers the estimated median persistence
to 27h12 (95% CI: 24h,59h), which overlaps the *camera-trap* estimates, and removes the significance of
road traffic volume in the Cox-PH model (HR=0.93 (95% CI: 0.90,0.97), z=-1.76, p=0.08). Although the
volume of traffic of these highways was comparable to some roads used in both manuscripts on roadkill
persistence, highways in France differ from other roads by a few characteristics: they are mostly fenced,
and it is not possible to park a car and step out on the road surface. Therefore, roadkill species and
mechanisms of removal on these roads may differ from the rest of the road network. Guinard et al.
(2012) found median persistencies of over a week for passerines and strigiforms carcasses on french
highways.

Conclusions

Citizen sciences appear to be a viable way of obtaining persistence estimates for roadkill, covering large
spatial scales at lower costs than experiments conducted by professionals. This preliminary experiment
only involved a handful of volunteers and showed that they managed to produce exploitable data. Citizen
science roadkill monitoring projects could use this method as a way to estimate persistence in the
territory they cover by recruiting a sub-sample of volunteer contributors from the project. In this case, we
were able to cover a new type of road with this approach (highways), which leads to novel conclusions
about the spatio-temporal patterns of roadkill persistence.
<figure><img src="image_53.png" /><figcaption>Fig. 3: Roadkill persistence survival curves and 95% confidence intervals using two methods of carcass monitoring: volunteers observers checking for the presence of the roadkill carcasses at least every 24 hours, or placing motion-triggered camera traps in front of the carcasses to capture the moment of removal. The first method (n=86) estimated the median persistence time of roadkill at 10h (95% CI: 6h07,42h36) and the second method 43h (95% CI: 24h,62h).</figcaption></figure>

**Ethical statement**

In compliance with article 89 of the GDPR, we have received informed consent from the participants to use personal data. No personal data will be shared or stored after the end of this thesis project.

**Acknowledgments**

We would like to thank the volunteers that participated in this study, and in particular my colleague Prof. Y. Voituron for his enthusiastic regular reports of the state of the buzzard on highway A89. We would also like to thank the LPO Ain director, F. Bulliffon, as well as the LPO Auvergne-Rhône-Alpes as a whole for their help in recruiting volunteers. Finally, we are grateful for the implication of Bugey Côtière in the diffusion of our call to volunteers.
REFERENCES

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Guinard, É., Julliard, R., & Barbraud, C. (2012). Motorways and bird traffic casualties: Carcasses surveys and scavenging bias. *Biological Conservation*, 147(1), 40-51.

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.

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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.

Swinnen, K., 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. Pensoft Publishers.

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Chapter 3: Roadkill detection probabilities

<figure><img src="image_54.png" /></figure>
# Influence of animal size and driver attentiveness on roadkill detection rates (in preparation)

Annaëlle Bénard, Christophe Bonenfant and Thierry Lengagne

## CONTEXT

Counting roadkill carcasses is an important aspect of understanding and reducing animal mortality on roadways. In recent years, citizen science has emerged as a valuable tool for gathering large-scale data of the spatio-temporal distribution of roadkill. However, both professional and citizen science methodologies underestimate the actual number of roadkill, as not all carcasses present on the road are detected. If roadkill detection has been studied in the context of professional surveys, we have yet to explore the search efficiency of contributors in citizen science projects.

## OBJECTIVES

We conducted an experiment using taxidermied animals as roadkill decoys to:

- Estimate the rates of detection for roadkill based on the size of the carcass, the position of the observer in the vehicle, and the attentiveness of the observer,

- Illustrate the use of these estimated detection rates in the context of citizen science monitoring by correcting the roadkill reports from Faune-AuRA for two species of mammals (*Vulpes vulpes*, *Sciurus vulgaris*) and one bird species (*Buteo buteo*).

## KEY RESULTS

- The probability of detecting a carcass increased with its size, but the observer's position in the car had no impact on detection,

- Attentive drivers (focused on counting roadkill, e.g., professional monitoring) were found to have a much higher chance of detecting a carcass compared to inattentive drivers (i.e., normal day-to-day driving, citizen science) with a 34-fold difference.

- Reported roadkill in Faune-AuRA may represent only 10% of the actual number of collisions for the red squirrel *Sciurus vulgaris*, 49% of the collisions for common buzzards *Buteo buteo* and 28% for the red fox *Vulpes vulpes*.

## CONCLUSIONS

- Opportunistic roadkill reports tend to underestimate the true number of collisions due to the fact that not all carcasses are detected, and detection rates are even lower than in professional road surveys.

- Estimated ratios of reports-to-collisions in citizen science are alarming, especially considering the significant number of reports received by some of these projects (e.g., 30 reports daily to Faune-AuRA across all species).
Linear transportation infrastructures threaten the viability of many wildlife populations, most notably through directly mortality as a result of collisions with vehicles. Roadkill monitoring is a vital component of understanding and mitigating this mortality, and in recent years, citizen science monitoring has emerged as a way of obtaining large-scale datasets. However, in both professional and citizen science road monitoring, there is a possibility of under-estimating the number of roadkill because not all animals are detected, a bias that may be even greater in citizen science because contributors are not focused on searching for roadkill when driving. Using 20 taxidermied birds and mammals of varying sizes, we test the roadkill detection rates of drivers that were either tasked with detecting as much roadkill as possible, or that were unaware of the experiment at the time they drove by the decoys. We find that roadkill detectability increases with the size of the animal, and that drivers attentive to roadkill were 34 times more likely to detect carcasses than drivers that were not. We use the detectability estimates from inattentive drivers to correct roadkill reports from the citizen science database Faune-AuRA, and find that actual roadkill can be up to 9 times the number of reports present in the database. These results raise conservation concerns, considering that citizen science roadkill databases such as Faune-AuRA can receive hundreds of reports every day, many of them concerning endangered species.
# 1. Introduction

Terrestrial transportation contributes to the ongoing global biodiversity decline by jeopardizing the fitness of individuals (Forman & Alexander, 1998). In particular, building roads results in habitat loss and fragmentation that disrupt ecological processes (Seiler, 2001), and the acoustic, chemical and light pollution generated by road traffic are detrimental to the survival of many species (Bissonette & Rosa, 2009; Eigenbrod et al., 2009; Forman, 2000). Collisions between wildlife and vehicles are a direct source of mortality that potentially affects all terrestrial species (Olson et al., 2014; Swinnen et al., 2022) and threatens the persistence of populations (Grilo et al., 2021; Moore et al., 2023). The impact of road mortality could be as high as 194 million mammals and 29 million birds annually in Europe (Grilo et al., 2020). There is currently a need for reducing this impact, by identifying or the collisions hotspots and implementing mitigation measures. Fences can be used to prevent wildlife crossings roads, which also increases the fragmentation of habitats (Andrews, 2014). Structures such as underpasses and overpasses can be constructed to allow wildlife road crossing without the threat of encountering a vehicle (Bond et al., 2008). The high costs of these mitigation measures bring the need for accurate collision hotspot identification in order to target the most strategic locations (Teixeira et al., 2017).

For this reason, large-scale roadkill data is valuable. However, the collection of roadkill data in a territory requires regular monitoring of the entire road network, which is very costly (Henry et al., 2021). In recent years, ecologists have started to take advantage of the ubiquity of connected smartphones to ask road users to report the roadkill they encounter. Citizen science projects of roadkill monitoring have gained popularity in recent years, with some attaining thousands of contributors and 90 000 reports of roadkill (Swinnen et al., 2022). Although these projects allow the collection of roadkill data over large temporal and spatial scales, the reports from volunteer contributors often suffer from many biases. Incorrect species identification can introduce erroneous reports in datasets (J. L. Dickinson et al., 2010). Unevenly distributed sampling effort, both spatially and temporally, can introduce trends in roadkill reports that do not correspond to a biological reality (Hochachka et al., 2012). In addition, roadkill removal from roads by scavengers, people, or the passage of vehicles can prevent carcasses from ever being reported (Bénard et al., 2023). Finally, in most cases, not all roadkill is detected by contributors when they drive on the road (Collinson et al., 2014). Despite these issues, authors have argued that citizen science roadkill is useful for informing roadkill rates (Petrovan et al., 2020; Shin, Kim, Groffen, Woo, Song, & Borzee, 2022; Waetjen & Shilling, 2017).

In particular, detection probabilities for roadkill carcasses, which have received some attention from the perspective of roadkill survey conducted by professionals using standardized protocols (Collinson et al., 2014), are seldom addressed in citizen science. For standardized surveys, these detection rates can be
assessed by comparing the number of carcasses an observer can detect from a car to the number detected by observers on foot, which assumes that the latter can detect 100% of the carcasses present on the road (Teixeira et al, 2013). Roadkill detectability is better for observers on foot than in cars, increases with body size and differs depending on taxa (Collinson et al., 2014; Gerow et al., 2010; Santos et al., 2016; Teixeira et al., 2013, Table 1). Authors use these results to recommend optimal survey methods for maximum detection of roadkill (e.g., prioritize surveys conducted on foot, Collinson et al., 2014). In the case of citizen science however, the reporting of roadkill is usually opportunistic and cannot be standardized in this way. Instead, *ad-hoc* corrections of the dataset appear to be the most promising avenue for integrating roadkill detection into these databases (Gerow et al., 2010). However, roadkill detection rates are most likely context-dependent: professional observers during standardized surveys are actively looking for roadkill while contributors to citizen science projects are most likely not, as they report opportunistic sightings of roadkill during day-to-day activities. Current estimates of roadkill detectability have always focused on active roadkill visual search, but there are (to our best knowledge) no available estimates of the detection rates for drivers that are not in a context of active roadkill search.

Standardized professional surveys are sometimes conducted from cars with one person driving and one passenger surveying the road (e.g., Bright et al., 2015; Clevenger et al., 2002; Driessen, 2021), most likely for safety purposes, as it allows the driver to focus their full attention on the road. There is no clear evidence that passengers have higher roadkill detection rates when both are tasked with looking for roadkill (Collinson et al., 2014). In citizen science, we can make a different prediction: drivers typically focus their attention on the road ahead and to the left and right of the road (Chapman & Underwood, 1998; K. A. Miller et al., 2021), which is where roadkill is likely to be located, while we may expect passengers to be distracted or looking at the scenery. Therefore, there is a possibility that in this type of monitoring, drivers are in fact more efficient at detecting carcasses than their passengers.

In this study, we compare the detection rates of roadkill from a moving car in the context of citizen science, which means during non-active monitoring of roadkill, to rates in standardized roadkill surveys, which means drivers were tasked with detecting as much roadkill as possible. We used taxidermied mammals and birds as decoys to compare the detection scores of roadkill between both contexts, expecting that drivers in the context of citizen science will detect significantly fewer animals. Following current knowledge of the detectability of roadkill (Table 1), we expect decoy detectability to increase with its size in both contexts, and to differ according to the animal taxa. We also explore the differences in detection efficiency between the drivers and passengers of a vehicle during non-active monitoring, expecting better detection of roadkill from passengers. Finally, we demonstrate *ad-hoc* citizen science dataset correction by extracting roadkill reports from the citizen science database Faune-Auvergne-
Rhône-Alpes (Faune-AuRA) and using our estimates of the detection rates to account for the proportion of roadkill missed by the contributors of the citizen science project.

<table><caption>Table 1: Review of the roadkill detection probabilities during standardized professional road surveys. The detectability of roadkill from a moving vehicle can be estimated by either using decoys placed on the road or by comparing the results of surveys performed from a vehicle to surveys performed on foot, assuming that the latter represents a 100% detection probability. To the author's best knowledge, no estimates of detection probability are available for roadkill monitoring relying on citizen science.</caption><thead><tr><th>Authors</th><th>P<sub>detection</sub> (95% CI)</th><th>Method</th></tr></thead><tbody><tr><td>Collinson et al. (2014)</td><td>88.8% (83.8,92.8)</td><td>Painted pieces of rubber to simulate flattened roadkill placed on 1-km road transects on which observers drive</td></tr><tr><td>Gerow et al. (2010)</td><td>Amphibians: 4.8% (4,5.6)<br>Reptiles: 3.7% (2.6,4.8)<br>Birds: 34.5% (28.3,40.7)<br>Mammals: 10.3% (7.6,13)</td><td>Comparing the number of carcasses recorded during a driving survey to the number recorded during a concurrent survey on foot</td></tr><tr><td>Santos et al. (2016)</td><td>7% (2,15) for animals &lt;100g<br>13.3% (4,29) for animals &gt;100g</td><td></td></tr><tr><td>Teixeira et al. (2013)</td><td>Reptiles: 20% (7,33)<br>Birds: 5% (4.3,5.7)<br>Mammals: 47% (34,60)</td><td></td></tr></tbody></table>

# 2. Material and Methods

## 2.1 Roadkill decoys

We chose to use animal taxidermy to represent roadkill. This method ensured that the species' size and coloration were comparable to fresh roadkill, while also allowing the comparison of informed and uninformed roadkill detection using the exact same decoys. In addition, artificially placing decoys on the road instead of counting roadkill that was already present ensured that the exact number of carcasses available to detection is known, leading to robust estimates of detection probability. We acquired 20 decoys of 11 species of birds and mammals from private collectors or by having fresh carcasses (donated by a wildlife rehabilitation center) taxidermied by a professional or the authors themselves, in a posture consistent with realistic fresh roadkill (complete list of species in appendix A1). We used the volume of the decoy as a variable instead of body mass (Santos et al., 2016), as it was likely that the size of the animal is the variable influencing its detectability. We computed decoy volume using measures of length (head to tail), width (measured at the thoracic cage) and height (from end of foot, hoof, or paw to animal back, perpendicularly). In addition, we accounted for unique postures: for example, we included wing
span in some birds that had been taxidermied with their wings stretched out, which means they appeared
bigger than birds of the same body size with wings tucked along the body. During all experimental trials,
we placed the decoys on the roads shoulders or at the junction between the road surface and shoulder,
such that they were not hidden by the vegetation. We held the decoys in place using camping pegs. We
did not place decoys directly on the lanes for safety purposes, as vehicles may drive over them.

## 2.2 Roadkill detectability experiments

*Informed search*

We conducted an experimental session on the road network of in the Ain department, central-east France. We defined a 28km circuit of roads, on which the volume of traffic varied between 9989 vehicles.day⁻¹ to less than 500 vehicles.day⁻¹ and the speed limits varied between 90km.h⁻¹ and 50km.h⁻¹ (Ain departmental council, 2023). We placed 20 roadkill decoys in random order (selected with a random number generator) on the circuit, always on the side of the road the volunteer would drive on (fig. A2). We did not place decoys at less than 700m from each other, and avoided urbanized road verges for decoys not to be displaced or stolen.

The experimental session took place on an overcast morning with medium rainfall (2 to 8mm.h⁻¹) and no fog. We recruited 17 volunteers with no previous experience in professional roadkill surveys by advertising with the Ligue de Protection des Oiseaux (*Bird Protection League*, thereafter: LPO) NGO and a student naturalist association. We asked volunteers to drive at speed limit along the circuit with their personal vehicle. We timed the departure and arrival of each volunteer and compared it to a reference travel time when driving at speed limit to ensure no driver drove significantly slower or faster than recommended. There was at least a 10 minutes interval between the departures of one volunteer and the next such that vehicles did not directly follow one another. Each participant was asked to fill out a form twice with the list of species detected, the first time at the mid-point of the road circuit (after driving by 11 decoys for 14 km) and a second time after they had completed the circuit (additional 11 decoys, 14 km). We split detected decoys recording in two occasions to reduce the possibility of underestimating detection probability because of forgotten decoys.
## Uninformed search

In order to assess the detection rate of drivers who were not actively looking for roadkill, we conducted two experimental sessions during the general assembly (first session, n=59 participants, June 2022) and meeting of the board of directors (second session, n=15 participants, October 2022) of the LPO of the Auvergne-Rhône-Alpes region (thereafter: LPO AuRA). Note that most of the contributors in the citizen science roadkill project Faune-AuRA are members of the LPO AuRA (Faune France, 2018). We placed decoys in the last 5 kilometers before the meeting locations of each event, covering every possible itinerary, at least 30 minutes before attendees were expected to arrive. We ensured that all attendees would drive by similar decoys (same set of species) no matter the itinerary they used (fig. A3).

The first session included decoys of squirrels (*Sciurus vulgaris*) and foxes (*Vulpes vulpes*), and the second session included squirrels, foxes, diurnal raptors (European honey buzzard *Pernis apivorus* and common buzzard *Buteo buteo*) and pigeons (*Columba livia*). At the start of each meeting, we asked attendees to fill out anonymous surveys, asking them to disclose their seat in the vehicle they arrived in (driver or passenger) and whether or not they had seen roadkill in the last 5km of their journey. Possible answers included each decoy placed on their itinerary plus a control species (Eurasian badger) that was not present on the road, in order to control for false reporting (Bhattacherjee, 2012).

## 2.3 Data analysis

All analyses were performed using R 4.3.1 (R Core Team, 2023).

### Roadkill detectability in informed and uninformed searches

To evaluate the relations between decoy detectability, volume, taxa, and attentiveness of the observers, we constructed a mixed logistic regression (package *lme4*, Douglas et al, 2015). The response variable is the detection status after each encounter between a participant and a decoy (0: decoy was not detected, 1: decoy detected). Covariables included the volume of the decoy (continuous variable), the status of the observer (informed or uninformed observer) and the decoy taxa (bird or mammals), including all possible two-way interactions. We added the participant's ID nested within the experimental session as a random variable. We constructed models considering a linear and a log-linear relation between decoy volume and detectability, considering the hypothesis that detectability of a decoy does not increase linearly with its size, but rather increases at a fast rate for small to medium species, and approaches 100% detection at a
slower rate once animals are big enough to be detected by a large majority of the drivers. We compared
these models to all possible nested models to select the best models.

The effect of the observer's seat in uninformed searches

To test the hypothesis that passengers are more efficient at detecting roadkill during uninformed searches, we constructed a mixed logistic regression (package *lme4*, Douglas et al, 2015) modelling the detection status after each encounter between a participant and a decoy (0: decoy was not detected, 1: decoy detected) during the *uninformed* experiment only. Covariables included the volume of the decoy (continuous variable), the taxa (bird or mammal) and the seat occupied by the observer in the vehicles (driver or passenger) as well as all possible two-way interactions. We included the participant ID nested within the session as a random variable in the models. As described above, we modeled the effect of decoy volume on detectability as linear and log-linear to account for the possibility that animal detectability does not increase linearly with size. We compared these models to all their nested models to select the best models describing the data.

Model selection approach

We used a model selection approach with Akaike's Information Criterion (AIC, Akaike, 1998). This method selects a best model (defined as the model with the lowest AIC) and a set of best models (models with a ΔAIC = AIC<sub>model</sub> - AIC<sub>lowest</sub> < 2, Richards, 2005). The best models are the models that describe the data the best (goodness-of-fit) with as few covariates as possible (parsimony principle, Akaike, 1998). For each analysis, we report the 95% confidence set of the best models using AIC weights (Burnham et al., 2011). The selection of the best models was conducted using function *dredge* from package *MuMln* (Bartoń K, 2023).

Estimating the number of animal-vehicle collisions using citizen science data

Faune-AuRA is a database administered by the LPO AuRA and contains over 90 000 reports of roadkill from 333 species of mammals, birds, reptiles, and amphibians. We extracted the daily roadkill reports for 2022 of the 4 species used in the *uninformed* experiments: red fox (*Vulpes vulpes*), red squirrel (*Sciurus vulgaris*), common buzzard (*Buteo buteo*), and domestic pigeon (*Columba livia*). Following Teixeira et
al. (2013), the roadkill rate $\lambda$ (collision.day⁻¹) can be estimated as $\lambda = \frac{N}{p \cdot T_R}$, where N is the number of roadkill carcasses counted, p is the detection rate of the carcasses by the observer and $T_R$ is a measure of the carcass persistence on the road defined as the time needed to reduce the number of remaining carcasses on the road by $1 - \frac{1}{e} \sim 63\%$. We used the detection probability estimated for each of the species in the *uninformed* experiments, and previous estimates of carcass persistence to estimate $T_R$ (Bénard et al., 2023).

# 3. Results

## Roadkill detectability

In the *informed* experimental session, participants detected 59.9% (95% CI: 47.6, 72.1) of the decoys on average. In the *uninformed* sessions, a participant detected on average 10.1% (95% CI: 0, 31) of the decoys. None of the participants of the *uninformed* experimental session reported having seen the control decoy (not present on the road) in the surveys.

The set of best models included the decoy's volume (log-linear relationship), the observer's status (informed, uninformed), the taxa and interaction between decoy volume and status, and between decoy taxa and status (Table 2). The decoy volume and observer status were present in all the best models. The interaction between observer status and decoy taxa was not present in the set of best models. The top-ranked model included only the effects of decoy volume and observer status. In this model (intercept = 2.5e-4, 95% CI:8.2e-5,7.8e-4), the detectability of the decoys increased with the logarithm of their volume (Odds ratio (OR)=2.03, 95% CI: 1.80,2.28), and the odds of detecting a decoy when the participant was *informed* were 33.8 greater than when the participant was *uninformed* (95% CI: 23.2,49.3). The second-best model (ΔAIC=0.93) included an effect of the taxa, with greater odds of detection for mammals (OR=1.36, 95% CI: 1.01,1.82). The predictions of decoy detection score for *informed* and *uninformed* participants from the top-ranked models are represented in figure 1.
<table><caption>Table 2: 95% confidence set of the best models to describe the detection probability of roadkill decoys using Akaike's Information Criterion (AIC). Size = the volume of the decoy (continuous variable); IF = status of the observers, either looking for roadkill (informed) or not actively looking for roadkill (uninformed); Taxa = taxa of the decoy (bird or mammal). The last line gives the model that ranked last.</caption><thead><tr><th>Model</th><th>AICc</th><th>ΔAIC</th><th>w<sub>i</sub></th><th>acc w<sub>i</sub></th></tr></thead><tbody><tr><td>log(Size) + IF</td><td>468.68</td><td>-</td><td>0.210</td><td>0.205</td></tr><tr><td>log(Size) + IF + Taxa</td><td>469.61</td><td>0.93</td><td>0.128</td><td>0.33</td></tr><tr><td>log(Size) + IF + Taxa + log(Size) * IF + Taxa * IF</td><td>469.70</td><td>1.01</td><td>0.123</td><td>0.46</td></tr><tr><td>log(Size) + IF + Taxa + log(Size) * IF</td><td>469.72</td><td>1.03</td><td>0.122</td><td>0.58</td></tr><tr><td>log(Size) + IF + log(Size) * IF</td><td>469.81</td><td>1.13</td><td>0.117</td><td>0.69</td></tr><tr><td>log(Size) + IF + Taxa + Taxa * IF</td><td>470.05</td><td>1.36</td><td>0.104</td><td>0.79</td></tr><tr><td>log(Size) + IF + log(Size) * IF + log(Size) * Taxa + IF * Taxa<br>(full log-linear model)</td><td>471.28</td><td>2.60</td><td>0.057</td><td>0.85</td></tr><tr><td>log(Size) + IF + log(Size) * Taxa</td><td>471.63</td><td>2.94</td><td>0.046</td><td>0.90</td></tr><tr><td>log(Size) + IF + log(Size) * IF + log(Size) * Taxa</td><td>471.73</td><td>2.94</td><td>0.044</td><td>0.95</td></tr><tr><td>~ 1 (null model)</td><td>571.34</td><td>102.65</td><td>1.05e<sup>-23</sup></td><td>1</td></tr></tbody></table>
<figure><img src="image_55.png" /><figcaption>Fig. 1: The detection probabilities for roadkill decoys placed on roads of the Auvergne-Rhône-Alpes region, France, depending on the information status of volunteer observers. Volunteers were either actively looking for roadkill on a road circuit (informed), or the decoys were placed on roads without their knowledge (uninformed). A logistic regression shows that informed volunteers detected more roadkill decoys, and the detection probability of a decoy increases with its volume. The list of decoy species, represented here by silhouettes, is available in appendix 1.</figcaption></figure>

Passenger versus driver searching efficiency

In total, 85.2% of the participants reported that they were driving the vehicle when going to the meetings, and the rest reported themselves as passengers. One participant reported going to the meeting on a motorcycle and was assigned to the *driver* category. The drivers detected on average 11.7% of the carcasses (95% CI: 0, 33.3) and the passengers detected 7.8% of the carcasses (95% CI: 0, 27.9).

All the best models included the effect of decoy size, and all the best models save two included the effect of taxa (Table 3). However, there was little statistical support for differences in detection scores between drivers and passengers, as the variable was not present in the top ranked model and only appeared in two of the 6 models present in the set of best models. Note that these models correspond to the linear and log-
linear versions of the same set of covariables. The top-ranked model (intercept: OR=0.08, 95% CI: 0.36,0.19) only included the effect of decoy volume and taxa. Decoy detectability increased with its size (OR=1.88; 95% CI: 1.46,2.44) and the odds of detecting mammal decoys were 1.53 higher than bird decoys (95% CI: 0.77,3.04). In the model including the observer's seat, the odds of a driver detecting a decoy were 1.42 higher than a passenger (95% CI: 0.86,2.33).

<table><caption>Table 3: 95% confidence set of the best models to describe the detection probability of roadkill decoys using Akaike's Information Criterion (AIC). Size = the volume of the decoy (continuous variable); Size = the size of the decoy (cm³); Taxa = taxa of the decoy; Seat = seat of the observer in the vehicle (driver or passenger). The last line is the model that ranked last.</caption><thead><tr><th>Model</th><th>AICc</th><th>ΔAIC</th><th>w<sub>i</sub></th><th>acc w<sub>i</sub></th></tr></thead><tbody><tr><td>Size + Taxa + Size * Taxa</td><td>126.62</td><td>-</td><td>0.129</td><td>0.129</td></tr><tr><td>log(Size) + Taxa + log(Size) * Taxa</td><td>126.62</td><td>4e<sup>-8</sup></td><td>0.129</td><td>0.259</td></tr><tr><td>Size</td><td>127.29</td><td>0.671</td><td>0.093</td><td>0.352</td></tr><tr><td>log(Size)</td><td>127.89</td><td>1.269</td><td>0.069</td><td>0.421</td></tr><tr><td>Size + Seat + Taxa + Size * Taxa</td><td>128.25</td><td>1.626</td><td>0.057</td><td>0.478</td></tr><tr><td>log(Size) + Seat + Taxa + log(Size) * Taxa</td><td>128.25</td><td>1.626</td><td>0.057</td><td>0.478</td></tr><tr><td>Size + Seat + Taxa</td><td>128.97</td><td>2.349</td><td>0.040</td><td>0.617</td></tr><tr><td>log(Size) + Seat + Taxa</td><td>129.00</td><td>2.374</td><td>0.039</td><td>0.697</td></tr><tr><td>log(Size) + Taxa</td><td>129.11</td><td>2.478</td><td>0.038</td><td>0.734</td></tr><tr><td>log(Size) + Seat</td><td>129.51</td><td>2.885</td><td>0.031</td><td>0.765</td></tr><tr><td>log(Size) + Seat + Taxa + log(Size) * Taxa + Seat * Taxa + log(Size) * Seat (full log-linear model)</td><td>129.77</td><td>3.150</td><td>0.027</td><td>0.792</td></tr><tr><td>Size + Seat + Taxa + Size * Taxa + Seat * Taxa + Size * Seat (full linear model)</td><td>130.00</td><td>3.377</td><td>0.024</td><td>0.816</td></tr><tr><td>Size + Seat + Size * Seat</td><td>130.54</td><td>3.917</td><td>0.018</td><td>0.834</td></tr><tr><td>log(Size) + Seat + Taxa + log(Size) * Seat</td><td>132.12</td><td>5.491</td><td>0.008</td><td>0.947</td></tr><tr><td>Seat + Taxa</td><td>136.23</td><td>9.609</td><td>0.001</td><td>1</td></tr></tbody></table>
## Roadkill rate estimates from citizen science data

We extracted 612 records of roadkill for *Vulpes vulpes*, 336 reports for *Sciurus vulgaris* and 276 reports for *Buteo buteo* from the Faune-AuRA database for the year 2022. Due to the lack of a detection rate estimate for pigeons *Columba livia* (none of the participants detected the pigeon decoy during the experiments), we did not compute estimates of the road mortality for this species. The characteristic time of carcass persistence on the road used here is 40h37 (95% CI: 10h37, 90h27) across all species (Bénard et al., 2023). Collisions estimates are presented in table 4.

<table><caption>Table 4: Estimated number of collisions with vehicles in the Auvergne-Rhône-Alpes region, France. We used citizen science reports of roadkill from the Faune-AuRA database for the 2022. Following Teixeira et al. (2013), we adjusted for the number of carcasses not reported with estimates of the detectability of animal carcasses in the context of citizen science derived from this study ($P_{\text{detection}}$) and of their persistence time on the road following a collision ($T_R$, Bénard et al., 2023).</caption><thead><tr><th>Species</th><th>P<sub>detection</sub><br>(95% CI)</th><th>T<sub>R</sub><br>(95% CI)</th><th>Number<br>of<br>reports</th><th>Estimated number of collisions<br>(95% CI)</th></tr></thead><tbody><tr><td>Vulpes vulpes</td><td>0.165<br>(0.123,0.207)</td><td rowspan="3">40h37<br>(10h37,90h27)</td><td>612</td><td>2196.7 (786.2, 11278.1)<br><b>0.81</b> (0.29,4.17) collisions per 100 km</td></tr><tr><td>Sciurus vulgaris</td><td>0.066<br>(0.037,0.094)</td><td>336</td><td>3016.6 (944.3,20422.7) collisions<br><b>1.12</b> (0.35,7.55) collisions per 100 km</td></tr><tr><td>Buteo buteo</td><td>0.294<br>(0.180,0.408)</td><td>276</td><td>554.3 (179.5,3459.6) collisions<br><b>0.21</b> (0.066,1.28) collision per 100 km</td></tr></tbody></table>
4. Discussion

Roadkill decoy detection rates were best explained by the size of the decoy and the information status of the observer. We confirmed that bigger decoys had higher detection rates by both observers actively looking for roadkill (**informed**) and observers not actively searching (**uninformed**). In addition, for decoys of the same size, **uninformed** participants were about 34 times less likely to detect them compared to **informed** participants. We found little statistical support for the hypothesis that drivers are better at detecting roadkill than their passengers in the context of **uninformed** searches because they are more focused on the road.

In a context of active roadkill search, which can be assumed to represent standardized professional road surveys, we show that not all roadkill will be detected by drivers, including large species (European badger *Meles meles*, red fox *Vulpes vulpes*). Professional monitoring is therefore likely to miss an important part of the roadkill (see also Santos et al., 2016), although note that we used inexperienced volunteers and the level of experience of the observer may impact the probabilities of detection (Collinson et al., 2014). However, even for trained observers, the detection of all roadkill carcasses from a moving car is not always possible for small specimens (Gerow et al., 2010). Here, we find for example that brown rats *Rattus norvegicus* will be detected 3.2 times less often than badgers *Meles meles* during a survey. Santos et al. (2016) estimated that carcasses < 100g were 2 times less detectable than larger species (> 100g). In consequence, if differences in roadkill detectability are not addressed, the representation of species in roadkill datasets is likely to be biased according to their size, with smaller species being under-represented. Some small-bodied species such as amphibians or bats, which pose significant conservation concerns (Frick et al., 2020; Sterrett et al., 2019), are particularly vulnerable to the potential consequences of underestimating the impact of road mortality on their populations.

Many of the available estimations of detectability in the literature are derived from the comparison of carcass counts conducted by an observer on foot versus an observer in a vehicle and consequently, these estimations only hold true if on-foot surveys can achieve a flawless detection rate (Teixeira et al., 2013). The validity of this assumption was challenged by Winton et al. (2018) who found that their walking survey detected only 76% of the snake carcasses deliberately placed on the road. Although one study has produced unbiased estimations of detection rates by instead placing decoys on stretches of road (Collinson et al., 2014), they used rubber pieces to simulate flattened fauna and limited range of decoy sizes. Wildlife varies in size, shape, coloration and some species have unique features such as spikes or feathers that can influence their visibility. This study is, to our best knowledge, the first to use life-like decoys covering a wide range of sizes. However, if taxidermied animals can be seen as reasonable proxies of freshly killed fauna, they are not suitable for assessing the detection probabilities of older
roadkill carcasses that have for example been flattened by vehicles (Bénard et al, 2023). The choice of methodology may also impact the conclusion regarding the detectability of the taxa. The best model to describe our data did not include differences in detectability between birds and mammals, although decoy taxa was present within the set of best models, providing limited evidence that mammals were more often detected than birds. While it is in line with Teixeira et al. (2013) where mammals found 9 times more detectable than birds, it is in contradiction with Gerow et al., 2010 (birds 3 times more detectable than mammals). Our methodology differs from both of these studies, as the authors used real roadkill already present on the road. It is possible that some species characteristics, such as bird feathers or hedgehog spikes, remain highly visible on the road even after the carcass has been flattened (AB personal observations), which may account for differences in detectability depending on the taxa.

In citizen science roadkill monitoring projects, contributors are most likely not always actively looking for roadkill, but instead opportunistically report the animals they detected during their everyday journeys. We have demonstrated that the detection rates of roadkill in this context differ markedly from active monitoring. The substantial difference in efficiency between active and uninformed observers results in a decreased probability for roadkill carcasses to be reported in a citizen science database compared to a dataset acquired by professionals. We find collision estimates, after correcting for detection and persistence of the roadkill, that were 2 to 9 times the number of carcasses that had been reported by contributors. This is alarming considering that some citizen science datasets reach ten of thousand of reports a year. Citizen-science databases most likely represent a fraction of the true number of collisions in a territory. Once again, this may be particularly prevalent for smaller species (passerines, amphibians, bats, rodents...), which are both the least detectable but also the fastest to disappear from roads (Santos et al., 2011, Bénard et al., 2023). We found little statistical support for the hypothesis that drivers are better at detecting roadkill than their passengers, which simplifies the correction process of citizen science databases, as the position of the observer in the vehicle at the moment of detection is not an information typically required in monitoring schemes (Olson et al., 2014; Swinnen et al., 2022).

While citizen science datasets suffer from many biases, such as imperfect detection of roadkill, incorrect species identification and uneven sampling effort, they can still inform collision rates at much larger scales than other sources of roadkill data (Schwartz et al., 2020; Shin, Kim, Groffen, Woo, Song, & Borzee, 2022; Swinnen et al., 2022; Waetjen & Shilling, 2017). As demonstrated in this study, it is possible to approach more realistic collision rates by correcting citizen science data for biases such as imperfect detection rate and roadkill persistence rate. Of course, our estimates operate on the generous assumption that one contributor will drive on every road of the Auvergne-Rhône-Alpes region every day. While it may be true on average, this assumption does not hold at local scales on small roads. More
accurate estimates could be obtained by also accounting for the distribution of the observation pressure in the region. In addition, there is a possibility that even detected carcasses are not always reported by contributors, which would further widen the gap between the number of reports and the actual number of collisions between fauna and vehicles.

**5. Conclusions**

Roadkill detection probability varies greatly depending on the size of the animal, but also in which the observer. Driver that are actively looking for roadkill, for example during standardized professional roadkill monitoring, detect 34 times more animals on the road compared to drivers that are not in a context of active roadkill monitoring. In citizen science monitoring projects, most drivers are most likely in the latter category, which means that they only detect a fraction of the roadkill present on the road. The difference between the number of roadkill reports and the actual number of collisions in the Faune-AuRA citizen science datasets could be as high as 60 times, meaning that this database, which can reach hundreds of new reports every day, only paints a very partial picture of the true magnitude of road mortality in wildlife.

**6. Ethical statement**

The participants recruited in this experiment gave free and informed consent for their answers to be used for the purposes of this study, in compliance with article 89 of the GDPR. Surveys were entirely anonymous and no other identifying data was collected.

**7. Acknowledgments**

We thank the volunteers for participating in this study, as well as J. Barbe for helping in the *informed* experiment. We also thank the Tichodrome wildlife rehabilitation center for providing carcasses and J. Barbe, A. Salis and Y. Voituron for helping with the taxidermy process. In addition, we thank J. Gilbert for his taxidermy work and expert advice.
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# Appendix

Appendix A1: **Complete list of the decoy species – volume of the decoy.** These decoys were used for the detectability experiments. Because the diurnal raptors (Red kite, Common buzzard, Honey buzzard, Marsh harrier) were very similar in coloration and volume, the participants could not accurately discriminate the species during the experiments. Consequently, their size was averaged, and they were pooled into a ‘Diurnal raptor’ category.

* 2 x Brown rat (*Rattus norvegicus*) – 756 cm³

* Common buzzard (*Buteo buteo*) – 9605 cm³

* 3x Common pheasant (*Phasianus colchicus*) – 11 880 cm³

* 2x Domestic pigeon (*Columba livia*) – 2025 cm³

* European badger (*Meles meles*) – 1 8720 cm³

* 2x European hedgehog (*Erinaceus europaeus*) – 633 cm³

* European honey buzzard (*Pernis apivorus*) – 9500 cm³

* Eurasian jay (*Garrulus glandarius*) – 3000 cm³

* 2x European pine marten (*Martes martes*) – 1 536 cm³

* Peregrine falcon (*Falco peregrinus*) – 3 366 cm³

* 2x Red fox (*Vulpes vulpes*) – 14 620 cm³

* Red kite (*Milvus milvus*) – 9410 cm³

* 2x Red squirrel (*Sciurus vulgaris*) – 1 110 cm³

* Western marsh harrier (*Circus aeruginosus*) – 8 902 cm³

<figure><img src="image_56.png" /><figcaption>Fig. A1: Roadkill decoys of a red fox (left) and red kite (right) placed on roads during the ‘informed’ experiments and equipped with a camera-trap to discourage theft.</figcaption></figure>
<figure><img src="image_57.png" /><figcaption>Fig. A2: Map of the informed experiment.</figcaption></figure>
<figure><img src="image_58.png" /><figcaption>Fig. A3: **Map of the uninformed experiment (June session).** Attendees of the general assembly meeting had no choice but to drive by a fox and a squirrel decoy on their way to the meeting location, which enabled the estimation of the detection probability of roadkill decoys without informing the drivers that roadkill would be present.</figcaption></figure>
Chapter 4: Reporting probabilities in opportunistic roadkill monitoring

<figure><img src="image_59.png" /></figure>

<figure><img src="image_60.png" /></figure>
# Case study: a methodology for estimating reporting probabilities in citizen science

## CONTEXT

Most WVC monitoring schemes relying on citizen science gather data through the opportunistic reporting of carcasses. Contrary to more standardized surveys, this type of reporting does not ensure that contributors consistently report the carcasses they have detected. The lack of *willingness to report* has long been suspected of being a source of bias in citizen science, but has never been formally demonstrated in the case of roadkill reporting.

## OBJECTIVES

- Estimate the conversion rate from detected to reported roadkill in opportunistic monitoring.

- Integrate this conversion rate into the Faune-AuRA dataset for select species to provide collision estimates.

## RESULTS

Only a fraction of the detected roadkill is effectively reported by contributors of the Faune-AuRA project, which further widens the gap between reports and actual collisions.

Although the method presented here is promising, we encountered difficulties and were not able to test the existence of selective reporting biases for charismatic or rare species.

## CONCLUSIONS

There is a reporting bias within Faune-AuRA where even identified carcasses go unreported. This concern may also extend to other citizen science monitoring initiatives, both regarding roadkill and biodiversity monitoring in general.

Methodological refinements are needed to provide more precise estimates and confirm the existence of selective biases in the willingness to report.
Introduction

In the previous chapters, we have discussed monitoring roadkill by examining the impact of roadkill persistence and detectability on the reliability of the data. While the focus of this work is primarily on citizen science, both of these issues are equally relevant to professional roadkill monitoring schemes, and have received some attention from the scientific community (e.g., Collinson et al., 2014; Santos et al., 2011). We have yet to address another potential source of bias that is seldom acknowledged in WVC citizen science research, and is inherent to opportunistic citizen science monitoring: the willingness to report roadkill. By "willingness to report", I refer to the probability of effectively reporting roadkill that has been detected. Opportunistic reporting in ecology is not limited to roadkill, and studies have shown that reporting probabilities in other projects are not constant, often resulting in positive biases toward rarer species (Henckel et al., 2020; van Strien et al., 2013). In addition, citizen science projects sometimes focus on charismatic species in order to make the tasks more appealing and recruit more volunteers (Pocock et al., 2015; Steger et al., 2017). In roadkill datasets, some authors have expressed their concerns that charismatic or less common species seems to be over-represented (Swinnen et al., 2022; Vercayie & Herremans, 2015). However, it is important to note that the representation of species in datasets, which should describe the specific roadkill rates, is also influenced by factors such as carcass persistence and detectability (Teixeira et al., 2013). To my best knowledge, there is no published evidence that not all detected roadkill is reported by contributors in roadkill opportunistic reporting, or that selective biases exist within the reporting process.

Assessing biases such as in reporting only part of the roadkill can be achieved by asking the contributors to self-report their rate of carcass reporting through surveys. We have examples of this methodology to assess the selective biases towards certain species in citizen science projects (Henckel et al., 2020). Self-reporting is also often used in fields such as epidemiology (self-reporting of symptoms, e.g., Menni et al., 2020) or social sciences (self-reporting emotions or behaviours, e.g., Ben-Shakhar et al., 2007). However, self-reporting methods may prove inaccurate, especially when regarding variables that are difficult to estimate accurately (such as the proportion of detected roadkill that is forgotten by the contributor and therefore never reported) or variables that can provide "an incentive to misreport", i.e., that may lead to contributors not being entirely truthful (Stone et al., 1999). In the case of roadkill reporting, there is a possible incentive to over-estimate the rates at which one reports detected roadkill, because of the perceived ecological and scientific importance of the data that is collected (Hsu & Lin, 2021). Therefore, accurate assessment of the willingness to report should not rely on self-reporting methods. In this case study, I will detail an experiment designed to assess the reporting probabilities in
the Faune-AuRA project without the need for self-reporting. The main hypothesis is that the detected-to-reported conversion rate is less than 100% (i.e., not all detected carcasses are reported) for most contributors (Swinnen et al., 2022).

# Material & Methods

## Measuring reporting rates

The primary difficulty in accurately measuring the reporting rate of contributors in a citizen science roadkill monitoring scheme is that it cannot be achieved when the contributors are aware that their reporting rates are being assessed. For this reason, the experimental protocol presented here shares similarities with the *uninformed detection probability* experiment outlined in manuscript n°4.

We conducted experiments during multiple LPO AuRA meetings (May-July 2022). These meeting took place in several departments of the Auvergne-Rhône-Alpes region: Ain, Loire and Drôme and provided an opportunity for multiple LPO members (which constitute a majority of the Faune-AuRA users) to convene at a predetermined location. The meetings gave us access to a pool of Faune-AuRA contributors that would be used as subjects in the experiments. We exclusively selected events that met the following criteria: (1) we could inventory all the possible itineraries leading to the meeting location, (2) the last few kilometers of these itineraries were composed of roads accessible to the experimenters (i.e., no highways or fenced roads), and (3) the meeting organizer agreed to cooperate in the experiment. Their cooperation involved providing the time and place of the meeting, as well as a restricted list of meeting attendees defined as “active Faune-AuRA roadkill contributors”, i.e., meeting attendees who had reported sightings of carcasses on roads to Faune-AuRA at least 3 times during the past year.

We used roadkill decoys in the form of taxidermied birds and mammals. These decoys are the same as presented in manuscript n°4: we procured carcasses from a wildlife rehabilitation center, and asked for the help of a professional taxidermist. During the taxidermy process, we tried to mimic the posture of a freshly killed carcass to produce decoys that would be mistaken for actual roadkill. Before each meeting, we placed the three to five decoys within the last 5 km of all possible itineraries to the meeting place. We randomly selected the order of the decoys (using a random number generator) and situated them on the side of the road that the attendees would drive by, either at the junction between the road and the shoulder or on the road shoulder itself. No decoys were placed directly in a lane due to safety concerns
for drivers. All decoys were positioned at least 30 minutes before the expected time of arrival of the first attendees, and removed once the meeting commenced. This methodology ensured that all meetings attendees had unknowingly driven by several roadkill decoys on their way to the meeting.

In the week following the meeting, I then contacted each meeting attendee from the list of “active Faune-AuRA roadkill contributors” via phone. During these conversations, I introduced myself as LPO member interested in road mortality and proceeded to ask the contributors (1) which route they took to reach the meeting location and (2) if they had encountered any roadkill in the last few kilometers of their journey. Simultaneously, we monitored the Faune-AuRA database to determine which of the attendees had reported decoys as roadkill prior to being contacted. We did not include roadkill reported after the subject had been contacted, as the questions asked during the phone call may have artificially increased the motivation of the subject to report a decoy they had detected. Therefore, for each decoy placed on the road during the experiments, we had obtained the list of active contributors that had driven by, as well as which of these contributors had detected the decoy, and which had reported it to Faune-AuRA (fig. 1).
1. Planned meeting where Faune-AuRA contributors will be present.

2. Roadkill decoys placed on all possible itineraries to meeting.

<figure><img src="image_61.png" /></figure>

<figure><img src="image_62.png" /></figure>

3. Faune-AuRA database is monitored for contributors reporting decoys as roadkill.

4. Contributors are contacted and asked about their itinerary and whether they saw roadkill.

<figure><img src="image_63.png" /></figure>

<figure><img src="image_64.png" /><figcaption>Fig. 1: Methodology of the experiment designed to measure the willingness to report from citizen science contributors of the Faune-AuRA roadkill monitoring scheme.</figcaption></figure>
## A brief introduction to Markov chains

A Markov process refers to a system that transitions between different states based on the probabilities assigned to these transitions. It exhibits a property known as *memorylessness*, where the future state solely relies on the present state and remains unaffected by past events. This concept is also known as the *Markov property*, named after mathematician Andrey Markov. The observed sequence of states in a Markov process is named “Markov chain”. You may encounter example of Markov chains in daily life, such as when typing on a phone: the predictive text feature on a keyboard guesses the next word based on the word currently being typed<fnref n="8" />. Here, each word represents a state, the entire English dictionary acts as the collection of all possible states in the Markov process, and the sentence is the Markov chain – a sequence of states selected from the pool.

Markov chain models are used to describe ecological processes such as ecological successions (from pioneer species to a fully functioning ecosystem, Logofet & Lesnaya, 2000) or capture-recapture methods for population density estimation (Pradel, 2005). In this chapter, I will only focus on explicit discrete-time Markov chains, i.e., Markov chains in which (1) the system transitions following discrete time steps $t = \{1,...,T\}$ and (2) all the states of the chain are known and observed.

The transition matrix of a Markov process contains all one-way transition probabilities between the states. In the earlier example of predictive text generation, the transition matrix would include the probability of transitioning from one word, such as "good," to another word, such as "morning", and would also contain a different probability of transition from "morning" to "good". In Markov models, an initial state is specified (e.g., the first word typed in a sentence), or a vector of initial probabilities for all possible states is used to randomly select the initial state.

<footnote n="8">8 This is a very basic form of predictive text generation. Modern predictive text features also rely on the general context of the text, or the typing habits of the user.</footnote>
Describing roadkill reporting as a Markov process

We consider the possible states of a roadkill carcass in relation to a contributor participating in a citizen science monitoring project. Any time a contributor drives by a roadkill carcass present on a road, the carcass will be in one of the following states: not seen, detected by the contributor, reported by the contributor. I will refer to these states respectively as “present” (but not detected), “detected” (but not reported) and “reported” (fig. 2). The Markov chain transitions between states at each time step $t = \{1, 2, 3\}$.

The following chains are possible:

* `{present, present, present}`: the animal has not been detected or reported by the contributor

* `{present, detected, detected}`: the animal has been detected but not reported by the contributor

* `{present, detected, reported}`: the animal has been detected and reported by the contributor.

<figure><img src="image_65.png" /><figcaption>Fig. 2: State transition diagram of the Markov chain used to describe the reporting process of a roadkill carcass. The initial state is “present”. The detection probability is denoted $\varphi$ and the reporting probability is denoted $\psi$.</figcaption></figure>
The initial state of the Markov chain is present. The transition matrix of the Markov process is defined as:

<table><thead><tr><th></th><th>present</th><th>detected</th><th>reported</th></tr></thead><tbody><tr><th>present</th><td>1-φ</td><td>φ</td><td>0</td></tr><tr><th>detected</th><td>0</td><td>1-ψ</td><td>ψ</td></tr><tr><th>reported</th><td>0</td><td>0</td><td>1</td></tr></tbody></table>

Where φ is the detection probability of the carcass by the contributor, and ψ is the reporting probability of said carcass conditional on its detection. Note that the probabilities for each individual row and column always sum to 1. The state *reported* is called an absorbing state, which means that once entered, it cannot be left.

## Bayesian modelling of detection and reporting rates

We aim at estimating two independent parameters from the transition matrix: the detection probability (φ) and the reporting probability (ψ). To estimate the transition matrix, we constructed a Bayesian multinomial model describing the data obtained from the experiments. We assigned uniform priors ranging from 0 to 1 for the variables φ and ψ (uninformative priors). We ran three concurrent Monte-Carlo Markov Chains (MCMC) for 10000 iterations. We discarded the first 3000 iterations and applied a thinning value of 3 to reduce autocorrelation. To evaluate the MCMC chains, we conducted posterior checks by visualizing trace plots and Gelman-Rubin-Brooks plots (Brooks & Gelman, 1998). We implemented the model using NIMBLE (de Valpine et al., 2017) for R (R Core Team, 2021).
The probabilities $\varphi$ and $\psi$ are useful to compute the conversion probability of an animal carcass present on the road into a data point in the Faune-AuRA database:

$$P_{conversion} = P_{detection} \cdot P_{report|detected} = \varphi \cdot \psi$$

Following Teixeira et al. (2013), we can construct the function G(t) that represents the number of carcasses present on roads at time t. Therefore, we can write:

$$\frac{dG(t)}{dt} = \lambda - \frac{G(t)}{T_R}$$

, where $\lambda$ is the roadkill rate (number of collisions per unit of time) and $T_R$ is a characteristic time describing the persistence of carcasses on roads. This equation leads to the steady state solution $G(t \to \infty) = \lambda \cdot T_R$, i.e. the number of roadkill carcasses present on roads is the product of the roadkill rate and the characteristic time of roadkill persistence.

Assuming that there are contributors present to report roadkill carcasses, the number of reported carcasses R(t) at time t is therefore defined as $R(t)=P_{conversion} \cdot G(t)$. We can write:

$$R(t \to \infty) = P_{conversion} \cdot G(t \to \infty) = \psi \cdot \varphi \cdot \lambda \cdot T_R$$

Using the methodology presented in manuscript n°3, we can estimate the roadkill rate $\lambda$ of a species using the number of roadkill reported to the Faune-AuRA database on a daily basis, the characteristic time of the carcasses $T_R$ (obtained from chapter 2) and the probabilities $\varphi$ and $\psi$. With this approach, we assume that each road of the AuRA region is surveyed daily by project contributors.

We estimated the road mortality for the red fox *Vulpes vulpes*, the red squirrel *Sciurus vulgaris* and the common buzzard *Buteo buteo* using the detectability estimates for these species from manuscript n°4, table 4, the characteristic times of persistence presented in manuscript n°3, as well as the reporting probability $\psi$ estimated in this chapter.
# Results

## Field experiments

We successfully obtained 76 observations regarding the detection and reporting rates of 7 different species of roadkill decoys (red squirrel *Sciurus vulgaris*, common buzzard *Buteo buteo*, domestic pigeon *Columba livia*, European hedgehog *Erinaceus europaeus*, pine marten *Martes martes*, brown rat *Rattus norvegicus* and ring-necked pheasant *Phasianus colchicus*) from 3 meetings (LPO departmental assemblies of the Ain, Drôme, and Loire departments). We interviewed a total of 27 contributors, who detected 5.2% of the decoys, resulting in only 1 report.

We encountered several challenges during the implementation of the experimental protocol. Despite regular patrols, some of the roadkill decoys were stolen from the road during the experiments. As a result, we were unable to determine which attendees had driven by the decoy, and had to exclude these observations from the dataset. Additionally, the number of active contributors reporting roadkill present at meetings was generally low (less than 10 individuals). Some contributors either did not disclose their contact information or could not be reached despite multiple attempts.

## Detection (φ) and reporting (ψ) probabilities

The model estimated the detection probability φ at 0.0332 (95% credible interval: 0.0111,0.0661, rhat=1, N<sub>eff</sub>=2951) and the reporting probability ψ at 0.3349 (95% credible interval: 0.0539,0.7216, rhat=1, N<sub>eff</sub>=3016, fig. 3). Collision rate estimates are presented in Table 1.
<figure><img src="image_66.png" /><figcaption>Fig. 3: Posterior distributions of the detection probability (φ) and the reporting probability conditional on the detection (Ψ) of roadkill decoys estimated with a Bayesian model of the Markov process.</figcaption></figure>

<table><caption>Table 1: Estimated number of collisions with vehicles in the Auvergne-Rhône-Alpes region, France. We used citizen science reports of roadkill from the Faune-AuRA database for the 2022, and adjusted for the number of carcasses not reported with estimates of the detectability of animal carcasses in the context of citizen science (P<sub>detection</sub>, chapter 3), of their persistence time on the road following a collision (T<sub>R</sub>, chapter 2, manuscript n°3) and of the proportion of detected carcasses actually reported by contributors (ψ, or P<sub>report</sub>).</caption><thead><tr><th>Species</th><th>P<sub>detection</sub><br>(95% CI)</th><th>T<sub>R</sub><br>(95% CI)</th><th>P<sub>report</sub><br>(95% CI)</th><th>Number<br>of<br>reports</th><th>Estimated number of collisions<br>(95% CI)</th></tr></thead><tbody><tr><td>Vulpes vulpes</td><td>0.165<br>(0.123,0.207)</td><td></td><td></td><td>612</td><td>6557.3 (1088.9,208852.8) collisions<br><b>2.42</b> (0.40,77.16) collision per 100 km</td></tr><tr><td>Sciurus vulgaris</td><td>0.066<br>(0.037,0.094)</td><td>40h37<br>(10h37,90h27)</td><td>0.335<br>(0.054,0.722)</td><td>336</td><td>9004.7 (1307.8,378197.5) collisions<br><b>3.33</b> (0.48,139.7) collision per 100 km</td></tr><tr><td>Buteo buteo</td><td>0.294<br>(0.180,0.408)</td><td></td><td></td><td>278</td><td>1654.5 (248.6,64065.9) collisions<br><b>0.61</b> (0.09,23.67) collision per 100 km</td></tr></tbody></table>
Discussion and perspectives

We conducted this study in the Faune-AuRA project to obtain objective measures of the reporting rates using roadkill decoys and semi-structured interviews. Our findings revealed that approximately 33% of detected species were reported in the database, with a large 95% credible interval spanning 5% to 72%. In consequence, while the difference between the number of reports in Faune-AuRA and the estimated number of collisions varied between 6-fold (common buzzard Buteo buteo) to 27-fold (red squirrel Sciurus vulgaris), the 95% confidence intervals around these estimates were wide enough to limit the relevance of these results.

Importance of reporting probabilities and biases in citizen science

Opportunistic reporting of species has gained significant popularity as a form of citizen science projects (e.g., Faune-France, observation.org, artportalen.se, eBird), producing large quantities of data as well as "contribut[ing] to public engagement with the environment, potentially leading to behavioural change" (Pocock et al., 2018, see also Tulloch et al., 2013). The idea that not all species are reported by contributors is not novel (van Strien et al., 2013), but is often grouped with other issues regarding opportunistic monitoring, such as unevenly distributed sampling efforts (Knape et al., 2022; Mair & Ruete, 2016). However, the spatial distribution of the sampling effort is sometimes approximated with proxies such as population density or site accessibility (Geldmann et al., 2016; Sicacha-Parada et al., 2021), but the presence of an observer is not a guarantee of the effective reporting of the species present in the area. Failure to separate sampling effort and reporting probabilities may therefore result in introducing false-negatives to the models. In addition, selective biases may conduct contributors to report some species more often than they do others.

The fact that participants do not always report all the species they detect in biodiversity monitoring schemes without standardized reporting protocols ("uncoordinated citizen science effort", Klemann-Junior et al., 2017) is likely to extend to roadkill monitoring as well. We demonstrated here that not all detected roadkill will be reported to the Faune-AuRA database, which further widens the gap between the number of wildlife-vehicle collisions and the number of roadkill reports. Contributors may be occupied or time-constrained when they come across a carcass, resulting in a delayed reporting to the database. Consequently, it is highly probable that some of these encounters will be forgotten and remain unreported. Additionally, for contributors who typically wait until reaching their destination to report
roadkill, instances of forgetting or not having sufficient time or motivation to report the carcasses may
also transpire.

Beyond general under-estimations of the number of roadkill, failure to report an animal may also impact
the accuracy of roadkill datasets from citizen science projects through selective biases for species or taxa.
Selective reporting has been described in other citizen science projects that focus on living specimens,
with contributors preferring to report species that are rarer (Steen et al., 2019; van Strien et al., 2013), or
in contrary, common species that are most likely more well-known and identifiable to contributors (Tiago
et al., 2017). Selective reporting would result in roadkill datasets being biased toward certain species,
that are readily identifiable to drivers through unique features, or that are perceived as conservation
concerns by the general population. The existence and quantification of selective biases in roadkill
monitoring is highly critical to proper exploitation of these databases, but has yet to warrant interest from
the scientific community.

We initially set out to test the hypothesis that certain species were preferentially reported due to their charisma or conservation status (Swinnen et al., 2022) by selecting same-sized pairs of decoy species that would represent a charismatic or rare species, together with a more common, invasive or injurious species (e.g., a peregrine falcon paired with a domestic pigeon, a honey buzzard paired with a ring-necked pheasant, and a red squirrel paired with a brown rat). However, some decoys from these pairs were stolen or removed during the experiments, limiting the number of contributors exposed to these pairs of species. In any case, having only one case of a decoy reported to the database, we are unable to draw any conclusions regarding the existence of a selective reporting bias in Faune-AuRA.

Assessment of the methodology

We discovered a significant disparity between the sampling effort initially anticipated and the actual
effort required to gather data. The data collected provided limited examples of roadkill reporting, which
had an impact on the predicting power of the Markov chain model. In consequence, the road mortality
estimates for *Vulpes vulpes*, *Buteo buteo* and *Sciurus vulgaris* have large uncertainty margins. Roadkill
reports for these species in Faune-AuRA could represent on average between 4% and 17% of the actual
collisions, but confidence intervals (Table 1) are too large to draw meaningful conclusions. As a point of
reference, Faune-AuRA receives on average 32 (± 9) roadkill reports daily across all species.

The estimated detection rates (3.3%, 95% credible interval: 1.1, 6.6) aligned with the *uninformed
detection rates* presented in manuscript n°4, where we estimated detection rates ranging from 4.1% (95%
CI: 1.8, 6.3, brown rat *Rattus norvegicus*) to a maximum of 23% (95% CI: 15.8, 25.9, European badger
*Meles meles*). The largest decoy used in this experiment was the red fox *Vulpes vulpes*. These low
detection rates indicate the need for a substantial sampling effort to achieve an adequate number of
detected decoys and determine reporting rates with reasonable accuracy. However, since both
experiments were conducted simultaneously, we did not have this information at the time we designed
this study. Another significant challenge we encountered was establishing contact with meeting
attendees, which is a crucial step for constructing the complete sequence of states of the Markov chains.
Eliminating the requirement for knowing the detection status of the decoy (and thus for contacting the
contributors) would greatly enhance the efficiency of this protocol. Although we presented a
methodology based on explicit Markov chains, it may be beneficial to explore hidden Markov chain
models instead, which would allow the detection status to remain unobserved, removing the need for
contacting the contributors (Glennie et al., 2023). Nevertheless, this study provides, to our best
knowledge, the first formal demonstration that there is a detected-to-reported conversion rate of less than
100% in roadkill monitoring schemes involving volunteers, as well as one of the first demonstrations of
this phenomenon across all unstandardized biodiversity monitoring schemes.

**Ethical statement**

This study involved collecting personal or identifying information. In compliance with article 89 of the
GDPR, this data will not be stored or shared.

**Acknowledgments**

We thank the presidents of the Ain, Loire, and Drôme LPO for their help in conducting this experiments
as well as the Tichodrome wildlife rehabilitation center and J. Gilbert for their help in procuring
taxidermy decoys of wild species.
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General discussion and perspectives

<figure><img src="image_67.png" /></figure>
<figure><img src="image_68.png" /></figure>

This thesis had for objective to work toward predictive models for wildlife-vehicle collisions on a large scale, both spatially and temporally.

In the first chapter, we developed a **mechanistic approach**, focusing on **animal and vehicle movement** and trajectories. The study successfully established **causal relationships** with factors that had previously been linked with wildlife-vehicle collision occurrences in empirical studies.

Additionally, we concluded that factors relating to **animal movement** are more promising than road and traffic characteristics in **explaining and predicting collisions**.

Throughout the remainder of the thesis, we focused on **improving the precision of roadkill data**.

While roadkill datasets serve as the foundation for predictive statistical models, they are also essential for validating mechanistic models.

We have focused on the citizen science Faune-Auvergne-Rhône-Alpes (Faune-AuRA) monitoring scheme to provide a **step-by-step investigation of the roadkill reporting process**, and work toward **robust correction parameters and methods** for the three main sources of bias we identified in opportunistic reporting. We conclude that the reports of roadkill present in the Faune-AuRA dataset paint a very partial picture of the magnitude of road mortality in the region.