# Navigating discreetly: Spatial ecology of urban wild boar in Bordeaux City's landscape of fear, France

Carole Marin, Jérôme Werno, Grégoire Le Campion, Laurent Couderchet

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## To cite this version:

Carole Marin, Jérôme Werno, Grégoire Le Campion, Laurent Couderchet. Navigating discreetly: Spatial ecology of urban wild boar in Bordeaux City's landscape of fear, France. Science of the Total Environment, 2024, 954, pp.176436. 10.1016/j.scitotenv.2024.176436. hal-04708478

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Submitted on 1 Nov 2025

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# Navigating discreetly: Spatial ecology of urban wild boar in Bordeaux City's landscape of fear, France

Carole MARIN<sup>a, *,</sup> Jérôme WERNO<sup>b</sup>, Grégoire LE CAMPION<sup>a</sup>, Laurent COUDERCHET<sup>c</sup>

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## HIGHLIGHTS

* Challenges in mitigating human-urban boar conflicts raise social demand for studies.

* We studied urban wild boar spatial ecology using a CMR campaign and GPS monitoring.

* Wild boar have become urban dwellers, strongly depending on urban forests.

* They show discretion with low mobility, nocturnality, and use of covered habitats.

* Some results deviate from the narratives regarding their spatial ecology.

## GRAPHICAL ABSTRACT

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## ARTICLE INFO

Editor: Rafael Mateo

**Keywords:**

Urban wild boar

Urban landscape of fear

Spatial ecology

GPS

Capture-mark-recapture

Narratives

## ABSTRACT

An exemplary urban adapter, the wild boar (*Sus scrofa*) has successfully colonized urban ecological niches worldwide. Improvement of strategies for optimal management of urban wild boar need to gather more empirical evidence of their spatial ecology. This study is based on GPS tracking and capture-mark-recapture (CMR) of 10 and 59 wild boar, respectively, captured in Bordeaux Metropolis (France). It shows that wild boar have become urban dwellers, with intra-urban home ranges varying from 1.3 to 64.6 km² (MCP 100 %) and from 0.5 to 9.6 km² (KDE 95 %), depending on urban conditions. CMR results confirm the low propensity to move away from urban areas (with a mean distance of 2 km between capture and recapture sites), despite a relatively low one-year survival rate since capture (47.5 %), primarily attributable to removal efforts. Wild boar strongly depended on urban woods, mostly during daytime resting, and highly frequented urban meadows during night foraging. Their use of urban agricultural areas was minimal, but they were mostly monitored following corn and grape harvests. Wild boar mitigated the risk associated with close proximity to humans by: a nocturnal activity (72.2 % of active locations registered from sunset to sunrise), which could also be partially attributed to their sensitivity to heat; a strong use of covered habitats, especially during daytime resting and when close to buildings and roads; and a low mobility during night-time foraging (1974 m average daily distance travelled). Moreover, we demonstrate high inter- and intra-individual variability in the spatio-temporal behaviour of urban wild boar.

<footnote><sup>a</sup> Centre National de la Recherche Scientifique (CNRS) - Laboratoire Passages CNRS 5319, 12 Esplanade des Antilles, 33600 Pessac, France</footnote>

<footnote><sup>b</sup> Ministère de la Transition écologique et solidaire, Gironde Hunting Federation - Capet, 33290 Ludon-Médoc, France</footnote>

<footnote><sup>c</sup> Michel de Montaigne University Bordeaux 3, Geography Department, Laboratoire Passages CNRS 5319, 12 Esplanade des Antilles, 33600 Pessac, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: carole.marin@cnrs.fr (C. MARIN), gregoire.lecampion@cnrs.fr (G. LE CAMPION), Laurent.Couderchet@u-bordeaux-montaigne.fr (L. COUDERCHET).</footnote>
Finally, we discuss the gap between these results and the narratives surrounding the spatial ecology of urban wild boar. Our results not only confirm the species' ability to adapt to urban environments, but also highlight their behavioural flexibility, underscoring the relevance of significant changes in representations and management activities to mitigate human-urban wild boar conflicts.

# 1. Introduction

Over the past decades, wild boar has successfully colonized human-made environments around the world, challenging social and ecological dimensions of wildlife management system (IWBS Scientific Committee, 2022; Von Essen et al., 2023). Major cities are affected by the process: Barcelona (Arregui, 2023; Castillo-Contreras et al., 2018; Conejero et al., 2019), Berlin (Stillfried et al., 2017a; Kotulski and König, 2008), Brandyburg (Oelke et al., 2022), Krakow (Basak et al., 2022; Konopinski et al., 2022), Budapest (Csokas et al., 2020; Zsolnai et al., 2022) and municipalities across Sweden (Von Essen and Redmalm, 2023a, 2023b) are examples where urban boar and social issues related to this unprecedented human-boar coexistence have been studied. Bordeaux (France) is no exception. First sightings of wild boar on the immediate outskirts of Bordeaux city happened in an urban protected area in the northern suburbs, in the mid-2000s. This emblematic game species has since become established throughout the entire Metropolis (Marin et al., 2024), giving rise to management difficulties and growing concerns among wildlife managers and urban stakeholders, more recently confronted with the nuisances associated with the species' vital needs (Marin et al., 2023).

Reflective of global environmental shifts, the wild boar's encroachment into urban area can be attributed to a myriad of factors. These include the overall demographic growth of wild boar populations (Massei et al., 2015), driven by their high prolificity (Servanty et al., 2007) and resilience to hunting pressure (Gamelon et al., 2011; Gayet et al., 2016; Keuling and Massei, 2021), with climate change also contributing to these trends (Vetter et al., 2020). In addition, the species demonstrates a high dispersal ability (Casas Díaz et al., 2013) and a remarkable capacity to colonize diverse environments (Acevedo et al., 2011; Markov et al., 2022), and its dietary flexibility (Ballari and Barrios-García, 2014) enables it to thrive in areas affected by agricultural abandonment (Hearn et al., 2014), to exploit cereal crops (Herrero et al., 2006; Lombardini et al., 2017; Schley and Roper, 2003) as well as urban anthropogenic food sources (Castillo-Contreras et al., 2021). The urban colonization process is further fuelled by ongoing urban development. In the backdrop of pervasive peri-urbanization, planning policies promote integration of nature into urban landscapes (Rosol et al., 2017; Kowarik et al., 2020). Conceptualized as ecological corridors in urban planning schemes, the interweaving blue and green spaces that traverse cities serve as crucial zones for the movement, reproduction, resting, watering, and feeding of diverse wild species (Heltai, 2013; Rega-Brodsky et al., 2022). Wildlife presence and abundance depend on urban morphologies (Fidino et al., 2021), intra-urban configurations (Beninde et al., 2015), animals' mobilities (Kays et al., 2015) and their propensity to use urban resources. Wild boar is one of these "urban exploiters" (Hagemann et al., 2022).

To thrive in the so-called urban “landscape of fear” (Laundre et al., 2010), wild boar must also be an “urban adapter” (Brogi et al., 2023). Mobile living organisms' movements result from interactions between external factors, individuals' internal state, species' and individuals' ability to move and navigational capacity (Nathan et al., 2008). First and foremost, morphological and biomechanical characteristics of both the species and the individual determine their ability to move. The internal state dictates individual motivations to move for feeding, resting, or reproducing, encompassing physiological stages (rut, gestation, lactation, growth, or senescence), health status, and stress levels. The navigation capacity relies on cognitive and sensory skills, notably developed in wild boar (Kornum and Knudsen, 2011; Maselli et al., 2014;

Masilkova et al., 2021). Finally, abiotic conditions and biogeographical characteristics of space play a decisive role in wild boar's mobilities (Brivio et al., 2017; Fattebert et al., 2017; Thurfjell et al., 2014). Although characterised by a high degree of human disturbance, urban heterogeneous landscapes comprise a wide variety of habitats offering shelter, anthropogenic and natural food, water and corridors to wild boar at relatively low search cost throughout the year (Castillo-Contreras et al., 2021; Stillfried et al., 2017c; Toger et al., 2018). Individuals that can effectively adapt their spatio-temporal behaviour to human-induced stress are more likely to survive and reproduce in urban environment, resulting in differences in behavioural (Breck et al., 2019; Gaynor et al., 2018; Ritzel and Gallo, 2020), genotypic (Hagemann et al., 2022; Stillfried et al., 2017b; Zsolnai et al., 2022), biological and phenotypic (Castillo-Contreras et al., 2021; Thibert-Plante and Hendry, 2011) traits of urban populations compared to rural ones.

While urban wildlife management often relies on transposing rural methods (Mc Cance et al., 2017) which effectiveness is rarely assessed (Pascual-Rico et al., 2021), defining effective management models for urban wildlife necessitates the integration of knowledge about urban population characteristics. In Bordeaux Metropolis, nuisance management policies imply increasing numbers of urban boar culls: from 291 in 2013 to 964 in 2021 (data from Prefecture and Hunting Federation of Gironde department). However, the steady growth of these removals over the past ten years has not been enough to stem the flow of animals into and within the city. In a local context where little is known about wild boar in urban environments, management policies rely on knowledge and experience of the rural wild boar. In contrast, some local hunters and individuals involved in wild boar removal possess detailed knowledge about the composition and distribution of local urban wild boar groups. However, conflicts between humans and urban wild boar and among humans about them give rise to contradictory and even accusatory narratives on the origin of animals and their habits (Marin, 2023). In this context, a growing social demand has arisen for scientific studies to objectively assess the spatial ecology of urban wild boar, aiming to specify management strategies.

This study relies on a combination of a capture-mark-recapture (CMR) program and a Global Positioning System (GPS) tracking to investigate the spatial ecology of urban wild boar in Bordeaux Metropolis. We investigated the following predictions: Wild boar in Bordeaux conurbation are urban dwellers (Prediction 1); Urban wild boar adapt their habitat use according to landscape characteristics while strongly depending on urban forests, especially during daytime resting (Prediction 2); in addition to using covered habitats (Prediction 2), urban wild boar exhibit stealth through minimal mobility paired with nocturnal foraging, while also displaying high activity due to human disturbance (Prediction 3); Urban wild boar display inter-individual and intra-individual variability in spatial behaviour, enhancing success in anthropized environments while diminishing predictability for wildlife managers (Prediction 4). Finally, we explored the gap between our results and narratives surrounding the spatial behaviour of Bordeaux urban wild boar. A four-year field survey, conducted in the manner of ethnographers in Bordeaux and Gironde department, allowed for the collection of speeches from 115 managerial actors (semi-structured interviews and participant observations) (Marin, 2023). The juxtaposition of narratives and factual elements enables an original enrichment of the discussion of each prediction.
## 2. Methods

### 2.1. Study area

This study was carried out in Bordeaux metropolitan area, in the south-west of France. Crossed by Garonne River, Bordeaux Metropolis comprises 28 communes covering 578km² and having a population of 819,604 in 2020 (INSEE, 2023). The radioconcentric morphology of Bordeaux conurbation reflects the historical growth of the city. Schematically, the ring road separates the central city and its suburbs from the outlying cities, which have discontinuous built-up areas, leaving more space for green areas. The most central part of the city, within the ring road, present a high building density, with 1723,5 dwelling units/km².

While Bordeaux Metropolis is pressured by an intensive urbanization process and a sustained population growth, recent metropolitan project proposes an urban plan based on interconnections of undeveloped areas and their inclusion in a wider network that connects the city with peripheral forest and agricultural areas (Brigant et al., 2018). We focused on two metropolitan parks. The Jalles Park was created in 2000, following the classification of the entire floodplain as unbuildable. It now covers 60 km² and shelters a National Nature Reserve (a'urba, 2019). The multifunctional valorisation of this park through the development of eco-responsible agriculture is part of the political agenda. On the right bank of the Garonne, the development of the Coteaux Park is part of the "Grand City Project", an urban renewal project launched in 2001. This park covers an area of 4 km² (GPV Rive Droite, 2021), mainly inside ring road's limits. Covered with a mosaic of agricultural environments, wastelands and forests, these two parks host a wide variety of

habitats that can meet the requirements of wild boar. We captured wild boar on 14 different sites located in and around these parks (Fig. 1).

### 2.2. Capture and handling

We performed two studies: a capture-mark-recapture (CMR) campaign and a Global Positioning System (GPS) tracking program. Both required capturing and handling wild boar.

We captured urban wild boar from 23 January 2020 to 11 March 2021 using cage traps, a live-capture method in line with the humane trapping standards outlined by Conejero et al. (2022). The cages were first installed and baited for a few days to acclimatize the animals. Once the doors were added, the wild boar triggered the trap by breaking a fishing line. Daily inspections and motion-detection cameras ensured quick responses, with operators arriving promptly in the morning to minimize time in the cage and handle animals during the coolest part of the day.

Wild boar sex was determined by visually inspecting external genitalia, and age was assessed using morphological criteria such as mammary gland development in females, tusk size in males, and overall weight, height, and conformation. The boar were classified into three age categories: juveniles (under one year), subadults (one to two years), and adults (over two years) (Gamelon et al., 2017). Two-thirds of the captured wild boar were estimated to be under one year old (Table 1). This age-related capture bias, noted by trappers and in the literature (Escobar-González et al., 2024), likely reflected the local population's demographics and the increased caution of older individuals. The GPS collars were fitted only to wild boar weighing at least 45 kg for females and 50 kg for males. With the GPS collars weighing 1 kg, the equipment-

<figure><img src="image_6.png" /><figcaption>**Fig. 1.** Map showing emplacements for wild boar captures in and around two urban parks of a radioconcentric agglomeration (Bordeaux, France). For each of the 14 capture sites, the proportional open circles indicate the number of ear-tagged wild boar, while the proportional filled circles specifically indicate those that were equipped with GPS collars in addition to an ear tag. The colours of the circles (whether open or filled) distinguish the monitoring zones (Jalles, Entre deux mers and Periurban).</figcaption></figure>
<caption>**Table 1**</caption>

Number of wild boar ear-tagged by sex, age class and monitoring zone. Numbers in brackets indicate the number of wild boar also fitted with GPS collars and tracked for at least 54 days.

<table><thead><tr><th rowspan="2">Monitoring zone</th><th colspan="3">Female</th><th colspan="3">Male</th><th rowspan="2">Total</th></tr><tr><th>Adult</th><th>Subadult</th><th>Juvenile</th><th>Adult</th><th>Subadult</th><th>Juvenile</th></tr></thead><tbody><tr><td>Entre-deux-mers</td><td></td><td>2 (1)</td><td>4</td><td>2 (2)</td><td></td><td>4</td><td>12 (3)</td></tr><tr><td>Jalles</td><td>1 (1)</td><td>4 (1)</td><td>10</td><td>1 (1)</td><td>2 (1)</td><td>10</td><td>28 (4)</td></tr><tr><td>Periurban</td><td></td><td>5</td><td>4</td><td>1 (1)</td><td>2 (2)</td><td>7</td><td>19 (3)</td></tr><tr><td>Total</td><td>1 (1)</td><td>11 (2)</td><td>18</td><td>4 (4)</td><td>4 (3)</td><td>21</td><td>59 (10)</td></tr></tbody></table>

to-body weight ratio was maintained at a maximum of 2.2 %, well below the typical 3–5 % recommendation (Soulsbury et al., 2020).

We chose not to anesthetize the captured urban wild boar to avoid public health concerns and the risks associated with induction, narcosis, and recovery (Barasona et al., 2013; Poulsen et al., 2018), especially considering the constraints of conducting pre-anesthetic assessments in field conditions. Captured boar were transferred to a custom-designed holding cage adjacent to the trap. This restraint cage featured a sliding wall to immobilize the animal, enhancing operator comfort, speeding up the procedure (averaging 10 min), and reducing the risk of injury and capture myopathy (Paterson, 2007). Handling required at least three operators: one monitored the boar’s respiration and behaviour (alert, agitated, or asthenic) and sprayed it with water to prevent hyperthermia. Once tagged, the animals were immediately released in an alert state.

## 2.3. Data process and capture final outcomes

We captured 59 wild boar (30 females and 29 males). Each captured wild boar was ear-tagged with a unique identification number and included in the CMR study. Among the 59 ear-tagged wild boar, 14 wild boar (6 females and 8 males) were also fitted with Lotek Litertrack Iridium 750 PB GPS collars (Fig. 1), programmed to record a location every 30 min. To ensure the quality of results, we performed successive steps of GPS data selection. We removed from the dataset those associated with a 2D least squares status and a Dilution of Precision (DOP) > 3.4, i.e. 25.8 % of the locations. Additionally, we retained only animals that were tracked for at least 54 days, resulting in the exclusion of 4 out of the 14 GPS-collared animals from the GPS-tracking analyses. Consequently, our final GPS dataset comprised 10 wild boar (3 females and 7 males), with a total of 1381 monitoring days and 34,059 locations.

We associated each GPS location with several variables: sex, season, outdoor temperature (°C) (recorded by the device), mean daily temperature (°C) (calculated from outdoor temperatures recorded by the device during the same day), time of day (dawn, day, dusk, night; according to the civil definition of the beginning of dawn and the end of dusk) and monitoring zone. Monitoring zone variable was constructed from the combination of landscape artificialisation and human disturbance within the home ranges (Minimum Convex Polygons, MCP 100 %). The MCP method considers all locations, including peripheral ones that reflect exploratory behaviour rather than areas essential for survival and reproduction (Nilsen et al., 2008). It may also include areas that the animal cannot actually access (Worton, 1987). In our study, the MCP method allowed us to assess the environmental characteristics of the animal’s immediate living spaces. Three monitoring zones were identified: “Jalles”, located in the densely built-up part of the urban area, characterised by a low human disturbance index and a moderate landscape artificialisation index, despite the fragmentation of the forest environment; “Entre-deux-mers”, also located in the densely built-up part of the study area, characterised by significant fragmentation of the forest environment and road densities, and high levels of human frequency and hunting drives pressure; “Periurban”, more peripheral, comprising large areas of forest with little fragmentation and characterised by moderate levels of human disturbance despite hunting drives. More information can be found in Supplementary Material 1.

Three wild boar were GPS-tracked in Entre-deux-mers (for a total of 417 days, resulting in 9767 locations), four in Jalles (for a total of 549 days resulting in 14,440 locations) and three in Periurban (for a total of 415 days, resulting in 9852 locations), primarily during autumn and winter (Fig. 2). Subsequently, we assigned each 59 tagged animals to one of three monitoring zones based on its capture location (Fig. 1). Table 1 presents the final capture outcomes of the wild boar included in the CMR campaign. Tags returned by hunters or authorities represented the recapture data. We present the results obtained 1597 days after the start of the campaign and 1184 days after the last capture.

## 2.4. Multiple experiments

Once the pre-treatments were completed, we tested our predictions by analysing urban wild boar space use (Prediction 1), habitat use (Prediction 2), risk-taking behaviour (Prediction 3) and inter- and intra-individual differences in spatial ecology (Prediction 4). To achieve this objective, we conducted a multi-track analysis of various factors related to the ecology of wild boar. Table 2 provides a summary, anticipating the presentation of the variables and analyses performed.

## 2.5. Study variables

### 2.5.1. Home ranges and dispersal (Prediction 1)

To explore Prediction 1, we constructed home ranges (10 wild boar GPS-tracked), using the Minimum Convex Polygon (MCP 100 %) and the Kernel Density (KDE 95 %) Methods. We supplemented the telemetry observations by measuring the shortest distances between tagging and recapture sites (59 wild boar included in the CMR campaign).

### 2.5.2. Functional habitats (Prediction 2)

Telemetry data can be used to analyse the selection of structural habitats that make up home ranges at a fine scale. To this end, we used the regional large-scale landcover database for Nouvelle-Aquitaine and grouped different types of landcover in six habitat categories: “forest”, “grassland”, “agricultural land”, “moors and heathland”, “urban area” and “wetland” (see Supplementary Material 1). We assigned each GPS location (34,059 locations) to the corresponding habitat category.

We measured the shortest distances from the edges of the nearest forest patches for each location (with locations within the forest assigned a distance of 0). Moreover, we characterised diurnal resting sites (RS) in an urban landscape of fear. To accomplish this, we selected “inactive” locations recorded during the day or the dawn (the activity variable is described in the following section). We created a heatmap from the point layer thus generated, and configured it to calculate point density within a radius of 15 m around each location. We polygonised this layer to obtain isodensity contours, retaining only those indicating densities >4 to identify the main RS. We then divided each polygon constructed (291 RS) within the landcover database.

### 2.5.3. Risk-taking behaviour (Prediction 3)

We continued by analysing the mobility and activity of animals fitted with telemetry devices. To this end, we constructed five predictor variables: “Daily Distance Travelled” (DDT, metres); “Speed of movement” (speed, metres/30 min); “Probability of activity” (P.act), 0 or 1); “Daily
<figure><img src="image_7.png" /><figcaption>**Fig. 2.** Monitoring schedule for the 10 GPS-tracked individuals, categorized by monitoring zone. Each wild boar is identified by a code (starting with WB for wild boar, followed by a number, and an indication of sex: F for female and M for male). The number in brackets following the code indicates the tracking duration in days. Autumn and winter accounted for the majority of the data, with 621 days of tracking in autumn (13,165 locations) and 540 days in winter (15,974 locations). Spring and summer accounted for 67 tracking days (2063 locations) and 153 tracking days (2857 locations), respectively.</figcaption></figure>

<table><caption>**Table 2**

Overview of study objectives, methods, sample sizes, variables and analyses performed.</caption><thead><tr><th>Objective (Prediction)</th><th>Refined prediction</th><th>Method (sample size)</th><th>Variables to be assessed</th><th>Analysis</th><th>Explanatory variables</th></tr></thead><tbody><tr><td rowspan="2">Space use (Prediction 1)</td><td>Low dispersal</td><td>CMR (59 individuals)</td><td>Distances between tagging and recapture sites</td><td>Mapping, Wilcoxon, Kruskal-Wallis and Dunn's pairwise tests</td><td>Sex, age and monitoring zone</td></tr><tr><td>Small and intra-urban home ranges</td><td>GPS (10 individuals)</td><td>Home ranges' sizes (MCP 100 % and KDE 95 %)</td><td>Mapping, Wilcoxon, Kruskal-Wallis and Dunn's pairwise tests</td><td>Sex and monitoring zone</td></tr><tr><td rowspan="4">Habitat use (Prediction 2)</td><td>Opportunism</td><td>GPS (34,059 locations)</td><td>Functional habitat category</td><td>Proportions, Graphing</td><td>Monitoring zone</td></tr><tr><td>Differences between diurnal and nocturnal periods</td><td>GPS (34,059 locations)</td><td>Functional habitat category</td><td>Proportions, Graphing, Chi-square test</td><td>Time, phase of day (day; night)</td></tr><tr><td>Dependence to forests</td><td>GPS (34,059 locations)</td><td>Number of locations near or within forests</td><td>Proportions</td><td>Distance from forest edges</td></tr><tr><td>Use of covered resting sites</td><td>GPS (291 resting sites)</td><td>Surface areas of habitat categories within resting sites</td><td>Proportions, Mapping</td><td>Monitoring zone</td></tr><tr><td rowspan="2">Risk-taking behaviour (Prediction 3)</td><td>Low mobility and enhanced activity</td><td>GPS (1381 days)</td><td>DDT, DAR</td><td>Description</td><td>Season, sex, monitoring zone, outdoor temperature, time of day</td></tr><tr><td>Nocturnality</td><td>GPS (32,148 locations)</td><td>Diurnality; ProbPERT</td><td>Description (for Diurnality); GAMM (for ProbPERT))</td><td>Season, sex, monitoring zone, outdoor temperature</td></tr><tr><td rowspan="4">Individual variability (Prediction 4)</td><td>Sensitivity to heat</td><td>GPS (1381 days)</td><td>DDT, DAR, Diurnality</td><td>GAMM</td><td>Season, sex, monitoring zone, outdoor temperature</td></tr><tr><td>Nocturnal foraging, depending on environmental characteristics</td><td>GPS (34,059 locations)</td><td>Distances from forest edges; Distances from buildings; Distances from roads</td><td>GAMM</td><td>Season, sex, monitoring zone, outdoor temperature, landcover category, type of road (for distances from roads), size of the nearest forest patch (for distances from forest edges)</td></tr><tr><td>Inter-individual variability in space use</td><td>GPS (4 individuals)</td><td>Home ranges' sizes (MCP 100 % and KDE 95 %)</td><td>Description</td><td>Individuals</td></tr><tr><td>Inter-individual variability in habitat use</td><td>GPS (10 individuals)</td><td>Functional habitat category</td><td>Proportions, Graphing</td><td>Individuals</td></tr><tr><td></td><td>Inter-and intra-individual variability in risk-taking behaviour</td><td>GPS (10 individuals)</td><td>DDT, Diurnality, Distances from buildings, and from roads</td><td>Graphing GAMM predictions</td><td>Individuals</td></tr></tbody></table>
activity rate" (DAR, %) and "Diurnality" (%). DDT is the sum of the shortest distances between two consecutive locations on the same day. Speed is the distance between two successive locations divided by the number of 30 min periods between them. For this variable, we removed locations recorded >3 h apart (32,148 locations retained). Mirroring similar studies (Miettinen et al., 2023; Thurfjell et al., 2014), we used the variable Speed to derive P.act). A value of 1 (for "active") was attributed to locations characterised by a movement speed above 15 m / 30 min; a value of 0 (for "inactive") for those characterised by a speed below this threshold. DAR is equal to the average of the values of P.act) over the same day, multiplied by 100. Finally, Diurnality is the pro- portion of "active" locations recorded from sunrise to sunset out of all active locations for the corresponding 24-hour period.

To assess distances from features within the urban landscape of fear, we used the distance measurements to forest edges presented in the preceding section. Additionally, we measured the shortest distances from buildings and roads (sections with motorised traffic only) for each location, using the French topographic database (2020), which provides a description of infrastructures with metric precision.

2.5.4. Inter- and intra-individual variability (Prediction 4)

We explored inter- and intra-individual variability for each of the first three predictions. Regarding Prediction 1, the 10 individuals were not tracked over the same seasons or for the same duration (Fig. 2). Therefore, we constructed the home ranges of one female (tracked in Jalles) and three males (one from each monitoring zone) during the same monitoring period. This period extended from November 8, 2020 to March 8, 2021, totalling 120 days of monitoring during the late autumn and winter seasons. Tracking for each individual began at least three weeks after capture. The analyses concerning the following pre- dictions were based on the previously presented variables.

2.6. Analyses

2.6.1. Home ranges and dispersal (Prediction 1)

We looked at the influence of sex and monitoring zone on home range sizes (MCP 100 % and KDE 95 %), and we analyzed the relationships between the capture-mark-recapture distances and sex, age and monitoring zone with Wilcoxon, Kruskal-Wallis and Dunn's pairwise tests.

2.6.2. Functional habitats (Prediction 2)

To test the hypothesis that wild boar, a generalist species, uses
habitats according to their availability in their immediate environment,
we first assessed the spatial distribution and the proportions of structural
habitats according to monitoring zones (see Supplementary Material 1).
Then, we compared habitat use according to the monitoring zones. We
plotted the number of locations recorded in each habitat category for all
animals in each zone and for each time window. By comparing these
three graphs, we were able to observe differences in habitat use not only
across monitoring zones but also at various times of the day. For this part
of the study, we considered daytime to be from dawn to dusk, and night-
time to be from the end of dusk to the beginning of dawn, as dawn and
dusk are short periods during which few data were recorded. In addition,
patterns of habitat use recorded at dawn and dusk were very similar to
those recorded during the day. For each monitoring zone, we calculated
the frequencies of day locations within each landcover category and did
the same for night locations. We then converted these frequencies into
percentages for easier interpretation. Finally, we tested the significance
of the differences between the day and night frequencies using a Chi-
square test.

To assess dependence on forest habitats, we calculated the pro-
portions of locations according to their distances to forest edges, irre-
spective of the time of day. Then, we calculated the areas of the different
habitat categories contained within the RS. We also counted the number
of RS according to their principal landcover category. We then mapped

the RS onto a satellite image background to 1) assess their spatial dis-
tribution and 2) detect whether they were located within small wooded
formations, not identified by the land cover database despite its fine
spatial resolution.

2.6.3. Risk-taking behaviour (Prediction 3)

We calculated the mean values of DDT, DAR and diurnality for comparison with the literature. Then, we used generalised additive mixed models (GAMM) for each variable to be predicted (binomial GAMM to study P.act)). To explore mobility and activity, we included the following categorical and continuous variables: season, sex, monitoring zone, outdoor temperature (°C) and time of day. To predict distances from urban features, we also included landcover categories. For the analysis of distances from forest edges, we considered the size of the nearest forest patch; for the analysis of distance from roads, we incorporated the type of road. We included the identity of the animal as a random factor. We built complete models. Then, we removed non-significant variables until obtaining a model with only significant ones. We selected final GAMMs based on a comparison of residual deviations and Akaike Information Criterion (AIC). Finally, we analyzed the predictions of the final models.

2.6.4. Inter- and intra-individual variability (Prediction 4)

To explore inter-individual variability in space use, we compared the
home range sizes of the 10 individuals tracked throughout their entire
monitoring period, and then compared the home ranges of the 4 in-
dividuals tracked during the same period. We then searched for indi-
vidual variability in habitat use within the same monitoring zone by
graphing the proportions of locations recorded in each habitat category
for each individual. Finally, we investigated both inter- and intra-
individual variability in risk-taking behaviour based on the prediction
plots from the final GAMM. The results from these analyses are inte-
grated into the sections dedicated to the first three predictions. How-
ever, they will be discussed in a separate, dedicated section.

For further technical details on the live-capture and handling
methods, as well as a reflective analysis of the variables and analysis,
refer to Marin and Couderchet (2024).

3. Results

3.1. *A low tendency to move away from the city (Prediction 1)*

Thirty-nine tagged animals were recaptured. The main cause of death was removal measures: administrative drives (17 individuals), trapping (5) and stalking (3), followed by hunting drives (10) and motor vehicle collisions (4). Of the 59 urban boar captured, 31 were recaptured within one year, resulting in a one-year survival rate since capture of 47.5 % for urban boar in the Bordeaux conurbation.

The average dispersal of individuals was just over 2 km, and 29
animals were recaptured within 2.5 km of the capture site (Fig. 3a).
Differences in dispersal distances according to monitoring zones were
not statistically significant. Similarly, there were no significant differ-
ences in dispersal distances according to combined characteristics of age
class at recapture and sex, nor by sex alone or by age class at death.

The sizes of home ranges varied greatly (Table 3; Fig. 3b). Differ-
ences between males and females were not statistically significant.
However, there were significant differences between zones (H₂ = 7, p =
0.030; same value for MCP and KDE home ranges), but only the differ-
ence between Jalles and Periurban was significant (Dunn's test, p =
0.035; same value for MCP and KDE home ranges). Variability in home
range size was also observed among individuals tracked over the same 4-
month period (Table 3).
<figure><img src="image_8.png" /><figcaption>Fig. 3. Maps showing: a) distances between capture and recapture sites and b) home ranges (KDE 95 %), according to monitoring zones. The proportional open circles indicate the number of ear-tagged wild boar, the proportional filled circles the number of wild boar recaptured, and the dashed lines the shortest distances between capture and recapture sites. Numbers in brackets indicate the total number of individuals captured, recaptured and measured for distances. Home ranges are represented in a different colour for each GPS-tracked wild boar.</figcaption></figure>

<table><caption>**Table 3**
Home ranges size (MCP 100 % and KDE 95 %; km²) by individual, sex, and monitoring zone. Values in brackets indicate sizes over the 120-day period.</caption><thead><tr><th>Monitoring zone</th><th>Individual</th><th>Sex</th><th>MCP 100 %</th><th>KDE 95 %</th></tr></thead><tbody><tr><td rowspan="3">Entre-deux-mers</td><td>WB01-M</td><td>Male</td><td>11.73 (10.12)</td><td>3.77 (3.44)</td></tr><tr><td>WB02-F</td><td>Female</td><td>14.63</td><td>6.09</td></tr><tr><td>WB03-M</td><td>Male</td><td>9.30</td><td>1.50</td></tr><tr><td rowspan="4">Jalles</td><td>WB04-M</td><td>Male</td><td>4.86 (4.86)</td><td>1.39 (1.38)</td></tr><tr><td>WB05-F</td><td>Female</td><td>1.27 (1.21)</td><td>0.46 (0.38)</td></tr><tr><td>WB06-M</td><td>Male</td><td>6.35</td><td>0.62</td></tr><tr><td>WB07-F</td><td>Female</td><td>1.48</td><td>0.53</td></tr><tr><td rowspan="3">Periurban</td><td>WB08-M</td><td>Male</td><td>64.57 (44.06)</td><td>9.60 (7.24)</td></tr><tr><td>WB09-M</td><td>Male</td><td>10.87</td><td>2.36</td></tr><tr><td>WB10-M</td><td>Male</td><td>60.50</td><td>15.31</td></tr></tbody></table>

### 3.2. Opportunistic habitat uses with a dependence on forests (Prediction 2)

Habitat use and coverage of resting sites varied according to monitoring zones. Detailed results of these studies are reported in Supplementary Material 2.

In a schematic manner, wild boar in Jalles retreated to forests during the day and foraged in grasslands at night (Fig. 4a). The percentage of locations recorded in forests was twice as high during the day compared to night; nocturnal use of grasslands was nearly three times that of daytime. In Periurban zone, wild boar predominantly exploited forests both during the day and night (Fig. 4b). Their use was significantly higher during the day than at night and the uses of grasslands and agricultural lands were slightly higher at night than during the day. Wild boar in Entre-deux-mers zone exhibited a more diversified use of

habitats (Fig. 4c). Their use of moors and heathlands was important, and it was significantly higher during the day than at night. Conversely, wild boar tended to wait until night-time to exploit grasslands and agricultural lands. Their use of grasslands was substantial, while their use of agricultural land, primarily represented by vineyards, was low. Finally, forest remained the most used environment, and differences between day and night were significant.

Considering all monitoring zones, nearly three quarters of locations (74.4 %) were recorded in forests or <80 m from a forest edge, and forests covered 74.8 % of the diurnal resting sites (RS). Grasslands and heathlands represented two other important types of habitats for the diurnal resting phase, especially in Jalles and in Entre-deux-mers, respectively. When available, as in Jalles, wetlands were also heavily selected for resting. On the other hand, agricultural areas were very sparsely selected. Finally, no RS were located within the urban land-cover category. While the selection of resting habitats varied depending on landscape characteristics of the different monitoring zones, diurnal RS were consistently found within covered areas, as depicted in Fig. 5.

Finally, examination of functional habitats at an individual scale revealed that wild boar within the same monitoring zone had similar use of urban areas, but differed in their use of the other five habitat categories (Fig. 6).

### 3.3. Urban boar, a stealth animal (Prediction 3)

The boar we tracked travelled an average of 1974 m per day and the DAR was 44.4 %. 27.8 % of their activity was diurnal. Subsequent analyses tested probability of activity, DDT, DAR, diurnality and distances from landscape features (forest edges, buildings and roads) as a function of various factors. Detailed results and characteristics of final GAMMs
<figure><img src="image_9.png" /><figcaption>Fig. 4. Opportunism in habitat use: a) Wild boar in Jalles used forests during the day and meadows at night; b) Wild boar in Periurban zone had a clear preference for forests; c) Wild boar in Entre-deux-mers preferred forests but also used moors and heathland, grassland and agricultural land. Graphs represent numbers of locations recorded in each habitat category for all the GPS-tracked animals in the respective monitoring zone.</figcaption></figure>

are presented in Supplementary Material 3.

Final GAMM to explain the probabilities of animal activity showed a significant positive effect of night and dusk compared to day and dawn and a significant positive effect of night compared to dusk (Fig. 7a). During the night, wild boar moved further away from the forest edge compared to dawn and daytime, approached buildings more closely than at dusk and during the day, and were closer to roads than during the other three phases of the day.

These findings align with the positive effect of outdoor temperature on distances from buildings and roads, and with the negative effect of outdoor temperature on both distances from forest edges and probabilities of activity, as higher temperatures correspond to daytime. By studying DDT, DAR and diurnality, we analyzed the effect of outside temperature, regardless of the time of day. The final GAMM predicting DDT showed that wild boar movement decreased steadily when the average 24-hour temperature was above 15 °C (Fig. 7b); analysis of DAR confirmed these result (Fig. 7c). On the other hand, diurnality was low at daily average temperatures below 15 to 20 °C but increased rapidly as the days got warmer (Fig. 7d).

All final GAMMs included season as a predictive variable. Specifically, we observed a positive effect of spring on distances from forest edges and a negative effect of summer on distances from forest edges, as well as from roads and buildings. Additionally, spring had a significant positive effect on DDT and DAR compared to the other three seasons. However, due to the limited number of data collected in summer and spring (Fig. 2), these results should be interpreted with caution. We

believe it is more prudent to rely on daily average temperatures when discussing DDT, DAR and Diurnality.

Monitoring zone was not included in any of the final models. Conversely, GAMMs revealed significant variations in distances based on landcover categories. First, the distance to the forest edge was greatest when the locations were in grasslands, moors and heathland or agricultural areas. In contrast, the locations recorded in urban areas were the closest to the forest edge. Secondly, locations within urban areas and heathlands exhibited the shortest distances from buildings and roads, while locations within forests were significantly further away from these features compared to those recorded in other landcover categories (Fig. 8). Furthermore, we observed significant shorter distances from main roads and highway ramps compared to gravel roads.

Sex was either not included in the final models or had no significant effect. On the opposite, final GAMMs' predictions showed a high degree of inter-individual variability in spatio-temporal behaviour (Fig. 9). Some of the more forest-dwelling animals displayed low mobility and high diurnality (WB07-F; WB09-M), while others were more mobile and either more or less nocturnal (WB08-M; WB10-M). Wild boar with an extensive use of open habitats such as grasslands were generally more mobile and nocturnal (WB04-M; WB05-F; WB06-M). WB06-M showed both a higher mobility and diurnality, but it stayed almost exclusively within the National Nature Reserve, where human disturbance is very low. In the most anthropized monitoring zone, wild boar were highly mobile. They were either strongly nocturnal (WB01-M; WB03-M), or more diurnal while keeping greater distances from roads and buildings
<figure><img src="image_10.png" /><figcaption>**Fig. 5.** Resting sites (RS) within covered habitats, even very small ones: a) In Entre-deux-mers monitoring zone, the selection of RS within small forest and heathland patches interspersed within the urban fabric led to a strong proximity to buildings and main roads; b) In Jalles monitoring zone, numerous RS were situated within grasslands and wetlands. RS found in heathland could be considered as part of grassland RS, as they were essentially situated beneath arboreal formations within grasslands. In both zones, many RS categorized as meadows were actually located within linear or very small wooded formations, undetected by the land cover database.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 6.** Variability in habitat use among individuals within the same monitoring zone. The graph shows the proportions of locations recorded in habitat category, for each individual.</figcaption></figure>

(WB02-F). Some individuals also exhibited strong intra-individual variability in their proximity to urban features (WB01-M; WB02-F; WB03-M; WB04-M; WB05-F) and/or in their daily activity rates (WB02-F; WB09-M). One plausible explanation for the intra-individual variability in WB09-M's daily activity is that its home range was located in an industrial zone, where human disturbance varies greatly according to the days of the week.

### 3.4. Main narratives regarding the urban wild boar phenomenon

Our field survey allowed us to gather spontaneous statements regarding the urban wild boar phenomenon, as expressed by 115 stakeholders. We categorized the respondents into five social groups based on the similarities in their narratives regarding the issue (see Supplementary Material 4). Table 4 summarizes the main narratives by major themes: i) the drivers of wild boar presence in urban nature, ii) the spatio-temporal behaviour of urban wild boar, and iii) the suggested solutions to address interspecific coexistence. Although the narratives varied among social groups, some recurrent opinions emerged. The most frequently expressed ideas were: overabundance of wild boar populations, loss of forestry habitat in the surrounding of the agglomeration, the species' adaptation to various environmental conditions, urban boar's affinity for moors and heathlands and feeding in urban and peri-urban crops. The majority of stakeholders expressed the need to reduce animal densities, without considering targeted culling based on weight, sex, or behavioural traits.

## 4. Discussion

### 4.1. Wild boar have become urban dwellers (Prediction 1)

Home ranges of wild boar in Bordeaux were among the smallest recorded in rural or urban areas (Csokas et al., 2020; Fattebert et al., 2017; Keuling et al., 2008), which emphasize the richness of natural and semi-natural urban environments. High-quality habitats with productive biotic resources often correlate with small home range sizes (Clontz

et al., 2022), while larger areas are required in poorer habitats to find food and shelter (Miettinen et al., 2023). Only one wild boar adopted a “ranging widely” movement pattern (Janeau et al., 1995), the others having intra-urban home ranges. The establishment of wild boar in urban areas was confirmed by the low dispersal of tagged individuals, regardless of the sex and age, averaging 2 km. The intra-urban location of home ranges and low dispersion confirm urban nature’s potential to meet the needs of this large mammal species. These findings align with studies demonstrating the existence of urban boar’s genetic clusters in major European cities (Hagemann et al., 2022; Stillfried et al., 2017b; Zsolnai et al., 2022), highlighting the wild boar’s status as an “urban dweller”, well-established in developed areas (Fischer et al., 2015).

In Bordeaux Metropolis, narratives on urban wild boar's spatial behaviour depend on the actors who articulate them. Supporters of urban hunting argue for its implementation by emphasizing the wild boar's search for areas of tranquillity. However, our results show a one-year mortality rate since capture exceeding 50 %, although likely underestimated, and primarily linked to administrative or hunting drives. Accused of contributing to the establishment and proliferation of wild boar in urban areas, managers of protected areas emphasize the presence of rural individuals passing through the city. Our results show that urban boar remained within the Metropolis boundaries, with few incursions into the periphery, whether they frequented un-hunted protected areas or not.

### 4.2. Opportunistic yet forestry animals (Prediction 2)

Wildlife encroachment in human-dominated environments is a multiscale process. In particular, the ecological niche of the species is shaped by the collection of local functional habitats (Gaillard et al., 2010). Previous studies indicate that the urban ecological niches of wild boar extend across most urban nature spaces in Bordeaux Metropolis (Marin et al., 2024). Telemetry data allowed us to analyse habitat use within intra-urban home ranges. Our results show that wild boar primarily used forests, grasslands, and heathlands. Their use varied greatly depending on the monitoring zone, indicating that Bordeaux's wild boar
<figure><img src="image_12.png" /><figcaption>**Fig. 7.** Urban boar are nocturnal and sensitive to outdoor temperature: a) Final GAMM predictions for activity probabilities by time of day; b) Final GAMM predictions for DDT as a function of temperature; c) Final GAMM predictions for DAR as a function of temperature; d) Final GAMM predictions for diurnality as a function of temperature. Grey-shaded areas represent the estimated standard errors.</figcaption></figure>

adapted to habitats available to them. Nevertheless, urban forests remained the favoured habitat. Overall, our analyses highlight a strong dependence of wild boar on wooded habitats, consistent with the findings of numerous studies conducted in urban or suburban (Ciach et al., 2023; Hagemann et al., 2022; Stillfried et al., 2017a; Wevers et al., 2020) and rural environments (Janoska et al., 2018; Morelle et al., 2016; Thurfjell et al., 2009).

Time of day emerged as particularly influential in shaping habitat use. Daytime resting sites' landcover varied across monitoring zones, but they were predominantly located in forest patches, including very small ones within the urban network, which is in line with Csokas et al. (2020) conclusions. Moors and heathlands were also used as covered resting sites, particularly in the most anthropized monitoring zone, allowing wild boar to rest in close proximity to main roads and residential sectors. Resting sites were consistently found under covered areas, even when classified as grassland, where small hedges and wooded formations (sometimes too small to be detected by the landcover database) provided the shelter. While wild boar using covered habitats in rural hunted areas experience lower human-induced mortality (Merli et al., 2017), our results align with those of Fradin and Chamaillé-James (2023), who demonstrated that wild boar frequently use covered habitats when resting near human facilities. Urban boar sought shelter under dense vegetation during the day and found food resources in and around forests during the night. Grasslands were frequently used by wild boar for their significant food sources during nocturnal feeding (Bueno et al., 2009), a pattern consistent with findings in other European cities (Ciach et al., 2023; Stillfried et al., 2017a). On the other hand, the use of urban agricultural spaces was minimal,

aligning with the observations of Stillfried et al. (2017a). Nevertheless, our results do not necessarily indicate a rejection of these habitats by wild boar. Cultivated cornfields offer food resources and shelter seasonally, becoming particularly attractive between the spring planting period and the summer harvest (Boitani et al., 1994). In our study, wild boar were predominantly monitored in autumn and winter, after the corn and grape harvests, and when forest fruiting were available.

Our results contrast with the two main narratives on the use of urban habitats by wild boar. First, most actors attribute the arrival of wild boar in urban areas to the accelerated urbanization on their natural habitat, forcing them to inhabit environments less favourable to them. Very few respondents consider the integration of nature into cities in recent development projects, with wooded corridors conducive to the settlement of numerous wildlife species. Secondly, damages to urban and peri-urban agricultural crops attributed to wild boar constitutes an economic argument widely mobilized to justify regulation. In rural hunting areas, damages to crops caused by large game animals are recorded, assessed, and compensated for by hunting federations, as provided for by French legislation. However, most central part of Bordeaux Metropolis has no hunting organizations. Consequently, information on damages is lacking, and there is no system for recording citizens' observations and complaints. While the GPS-tracked wild boar rarely frequented agricultural areas during their monitoring period, we cannot exclude the possibility of greater visitation by other animals and/or at different periods of the year. In addition, certain areas are particularly sensitive to the presence, even occasional, of the species. A single passage of a group through high-value crops incurs significant costs. Here, it is not the frequency of habitat use that determines human
<figure><img src="image_13.png" /><figcaption>**Fig. 8.** Illustration of results related to the distances of GPS locations from the edge of the nearest forest patch, buildings and roads, by landcover category. The distances to forest consider locations within the five other landcover categories (distances of those located in the forest are zero). The distances from buildings and roads consider locations within all six landcover categories. It is important to distinguish the urban area category from roads and buildings. Although the urban fabric includes these features of the urban landscape of fear, it can also contain small areas of trees, grass, and bushes, that are undetected by the landcover database despite its fine spatial resolution, but which could be used by wild boar.</figcaption></figure>

tolerance for coexistence, but rather how the wild boar use the habitat.

### 4.3. Discreet urban boar (Prediction 3)

Settling in wooded areas, urban wild boar exploit resources interwoven into the surrounding urban network, dealing with natural and semi-natural habitats fragmentation (Marin et al., 2024), and even benefiting from this fragmentation in certain urban environments (Castillo-Contreras et al., 2018). However, the dispersion of resources, combined with high exposure to human disturbances, poses increased risks in accessing them. In addition to resting under covered habitat, we identified three spatiotemporal behaviours that made wild boar discreet in a highly anthropized environment: i) low mobility; ii) nocturnal foraging; iii) proximity and use of covered habitats when foraging close to features of the landscape of fear.

The mean distance travelled per day by wild boar in Bordeaux was slightly <2 km, among the lowest reported in the literature (Brogi et al., 2022; Janoska et al., 2018; Olejarz et al., 2023; Podgórski et al., 2013). Wild boar dedicated over 50 % of their time to resting, consistent with results reported in the literature (Fradin and Chamaillé-James, 2023; Johann et al., 2020; Podgórski et al., 2013), thus contradicting our hypothesis that close proximity to human would lead to increased activity. Time of day was a determining factor in animal activity and movements. Our results confirm the predominantly nocturnal activity of wild boar inhabiting areas close to humans (Brivio et al., 2017; Ohashi et al., 2013; Rosalino et al., 2022; Wevers et al., 2020). Urban boar waited until nightfall to venture out of forests and to approach urban features. This risk-taking behaviour appeared to be moderated by: i) staying close to a forest edge when using urban area landcover category, and ii) the use of bushes as “relay” covered habitats near human infrastructures. Indeed, the closest locations to roads and buildings were situated within heathlands. Vegetal cover on roadside could also account for the significant shorter distances to main roads compared to gravel roads. Devoid of cyclists, walkers, and their dogs and associated with predictable disturbance from road traffic, urban boar in Bordeaux would benefit from vegetation associated with roadsides for shelter, as

observed in other cities (Ciach et al., 2023; Stillfried et al., 2017a).

Our results concerning active phases of urban boar align with those of Brogi et al. (2022), which revealed that rural individuals evolving in close proximity to humans compensate for this risk-taking behaviour by exhibiting a higher use of covered areas and lower mobility. By making wild boar harder to detect and minimizing the overlap between their activity periods and those of humans, their use of covered habitats and nocturnality may serve as strategies to avoid human encounters. This behaviour may also reflect the species' sensitivity to heat (Bracke, 2011). During the hot phases of the day, wild boar stayed in cool wooded environments while maintaining distances from buildings and roads, which could be linked to the nocturnality of wild boar. However, our results also indicate that activity and movements decreased significantly on hot days. Thus, this nocturnality could suggest that the species takes advantage of the cooler hours to be active, potentially conserving energy costs associated with thermoregulation (Brivio et al., 2017; Johann et al., 2020). On the other hand, a low diurnality implies exposure to human disturbances during the resting phases, that may lead to an increase of sleep fragmentation with potentially detrimental consequences on fitness (Olejarz et al., 2023). The higher diurnality recorded during hot days initially seems to contradict these analyses. However, the extended duration of daylight during hot periods may cause wild boar to begin their activity in the late afternoon, before dusk. Moreover, periods of drought could induce an increased search for less abundant and more dispersed water sources. This analysis aligns with the findings of Mortlock et al. (2022), indicating that during warmer and longer days, wild boar experienced a decrease in sleep quality and quantity, whereas rainfall appeared to enhance sleep quality. Finally, our analyses showed a negative effect of summer on distances from forest edges, which may be due to wild boar seeking cooler areas during hot days. Conversely, the positive effect of spring on these distances was likely due to increased nocturnal foraging in grasslands after the period of forest fruiting. Our analysis also revealed a closer proximity to roads and buildings during summer, suggesting that denser vegetation associated with road verges and interwoven in residential areas provide additional shelter and/or serve as ecological corridors. However, the small amount of data
<figure><img src="image_14.png" /><figcaption>**Fig. 9.** Inter and intra-individual variability in: a) Daily Distance Travelled; b) Diurnality; c) Distance from buildings; d) Distance from roads, according to the predictions of final models. From left to right: the first three individuals were GPS-tracked in Entre-deux-mers; the four following in Jalles; the three last ones in Periurban.</figcaption></figure>

recorded during spring and summer limits further analysis of the effect of habitat phenology on the spatial behaviour of urban boar.

A widely held discourse associates urban wild boar with heathlands, perceived as abandoned, neglected, and difficult-to-penetrate lost spaces. This representation of urban wild boar habitat justifies proposals to clear spaces presumed to be favourable to boar. However, wastelands serve as a refuge for a wide range of biodiversity (Bonthoux et al., 2014), and these proposals spark serious controversies among stakeholders. Our study shows that heathlands in Bordeaux do not represent the primary habitat of urban wild boar, which largely remain forest-dwelling animals, but it does provide an element of understanding to these representations. When available in urban areas, wild boar indeed use small overgrown spaces interspersed within the urban fabric as refuges or as covered relay points during night foraging. Signs of this presence in close proximity to human frequented areas are the most noticeable and probably the most sought after. It is not a matter of inventorying urban wild boar habitats, but rather what it is mainly observed. Clearing these spaces might decrease the proximity of urban wild boar to buildings and roads in some cases, but it would not address the broader issue of wild boar settlement in the city.

4.4. *Beyond common trends: animal personality (Prediction 4)*

The successful colonization of urban spaces by wild boar underscores their remarkable adaptation abilities. Three levels of behavioural plasticity are involved. Intra-specific behavioural plasticity is manifested by differences in common traits between urban and rural boar. Adaptive strategies also involve variation in behaviour within single populations,

encompassing both animal “personality” (referring to variation in responsiveness among individuals) and individual behavioural plasticity (denoting individual level of responsiveness to changing environmental conditions) (Dingemanse et al., 2010; Mathot et al., 2012). Related to cognitive flexibility, personality-dependent risk-taking behaviours play a crucial role in wildlife encroachment in human-made environments: in the same environmental conditions, inter-individual differences in boldness and exploratory behaviour contribute to individual niche specialization (Schirmer et al., 2019), with bolder individuals promoting resident migratory tactic (Found and St Clair, 2019). These individual behavioural adjustments eventually lead to evolutionarily differentiations at the population level (Ditchkoff et al., 2006; Wilson et al., 2020). The success of wild boar in colonizing anthropized areas may be explained by personality-related flexibility and behaviour plasticity, as evidenced by inter-individual differences in risk mitigation tactics (Brogi et al., 2022) and intra-individual plastic responses to temporal variation in human disturbance (Brogi et al., 2023). In our study, inter-individual variability was primarily manifested by variability in home range and in animals’ affinity for distinct functional habitats. Further analyses showed a high degree of variability in distances from urban features, mobilities, and diurnalities among urban wild boar.

Although our analyses do not allow for clear behavioural profiles to emerge, it raises two important points. First, wild boar’s use of urban space is a complex phenomenon that involves both inter-individual variability, which could be related to differences in individual perceptions of disturbances and risks taken to access resources, and intraspecific behavioural plasticity, enhanced by temporal variation in disturbances. This behavioural complexity makes the situations more
<table><caption>**Table 4**
Main narratives by social groups on urban wild boar: drivers, behaviour, and solutions.</caption><thead><tr><th>Themes</th><th>Social groups</th><th>1 - Managers, hunters and wildlife control operators affiliated with hunting associations</th><th>2 - Officials, territorial projects managers, administrative staff, civil servants, farmers representatives and privates</th><th>3 - Managers affiliated with ecologist associations, grounds managers and keepers</th><th>4 - Land use planners and architects</th><th>5 - Officials and territorial project managers committed to animal welfare</th></tr></thead><tbody><tr><td>Drivers</td><td>Suburban development and growing animal populations, coupled with the species' high adaptability lead to urban encroachment<br>Unhunted urban areas provide refuge, fostering populations growth on a larger scale<br>Rural individuals seek refuge in cities during the hunting season; some individuals have settled in the city, while others born in the city disperse back to rural areas</td><td>The overabundance of animals forces them to disperse beyond their rural forest habitat</td><td>The high mobility of wild boar allows them to visit urban areas, which become seasonally attractive based on weather conditions, although some individuals remain relatively stationary</td><td>Lack of awareness</td><td>The presence of wild boar in cities is multifactorial, driven by an overabundance of animals in rural areas due to species hunting management, combined with suburban expansion, anthropogenic food sources in urban areas, and the species' high adaptability</td></tr><tr><td>Behaviour</td><td>Night-time foraging in agricultural areas, causing significant damage<br>Dependence on moors and heathlands in unmanaged parcels and urban interstices<br>Increase in roads crossing during the night</td><td>Night-time foraging in agricultural areas, causing significant damage<br>Dependence on moors and heathlands in unmanaged parcels<br>Increase in foraging in residential areas during the day</td><td>Significant variation in habitat use depending on phenology<br>Increase in diurnal activity in recent years</td><td>Lack of awareness</td><td>Do not take a stance<br>Call for factual data, including records of damages, censuses, reports on removal practices, studies on habitat carrying capacities and ecology of the species</td></tr><tr><td>Solutions</td><td>Lethal solutions, which primarily involve hunting drives to reduce populations and to disperse urban boars<br>If drives are not feasible, spot trapping and culling through hide or stalk methods are considered<br>Habitat management through clearing dense vegetation areas</td><td>Lethal solutions, which primarily involve administrative removals<br>The suggested methods vary depending on the authorities, particularly regarding tolerance for drives to reduce populations<br>Trapping often recommended</td><td>Wild boar trapping to reduce their population while minimizing disturbance to other wildlife<br>Opposed to clearing dense vegetation areas</td><td>Lack of awareness</td><td>Opposed to reducing wildlife densities through lethal methods<br>Focus on reducing the attractiveness of urban areas for animals while creating a landscape of fear<br>When necessary and in specific cases, trapping can be used to avoid disturbing other wildlife</td></tr></tbody></table>

unpredictable than the official presentation of the issue and its solutions might suggest. Second, these observations raise questions about current management strategies, which primarily focus on increasing removal efforts to reduce wild boar densities, mostly through driven hunts that do not allow for considerations of individual characteristics (Vajas et al., 2020). Considering the animal dimensions in management can be based on both demographic and behavioural criteria. Targeting reproductive female helps to stabilize the demographic growth of a population (Gamelon et al., 2012). Nevertheless, it is the spatial and feeding behaviour of certain individuals that poses problems for coexistence. Rather than attempting to reduce urban ungulate densities, the implementation of proactive strategies aimed at targeting “bold” personalities well acclimated to human proximity and activities could limit the prevalence of resident and more “problematic” ungulates (Found and St Clair, 2019; Honda et al., 2018). In Bordeaux Metropolis, individuals causing more coexistence problems are notably those that maintain the least distance from urban infrastructures, those that slightly (though minimally) frequent agricultural spaces and/or urban landcover category, and the most diurnal individuals. However, identifying these individuals prior to removal requires profound transformations in hunting practices. Additionally, embedding targeted removal as a management measure would require both estimating the cost-effectiveness ratio for each method (Escobar-González et al., 2024) and significant changes in collective representations of effective management.

## 5. Conclusion

Forest game, nuisance in agricultural spaces, or liminal creature of urban woods, the wild boar seems to adapt to almost all contexts and continues to evolve alongside humans. Urban environments provide

favourable conditions for highly adaptable species like wild boar, whose spatial ecology in urban ecological niches remains incompletely understood. The coexistence between humans and urban wild boar exerts pressure on the management sphere, in a context of uncertainties regarding the origin and spatiotemporal behaviours of the animals that need to be managed. The primary objective of this study was to alleviate some of these uncertainties by gathering empirical knowledge on the behaviour of urban boar. Our results deviate from the prevailing narratives and representations regarding the ecology of urban wild boar, although these narratives still influence current management strategies. Opportunistic and flexible behaviours add to the challenges in management, underscoring the necessity of exploring alternative methods grounded in adaptive and circumstantial strategies. This indicates the significance of both considering micro-environmental conditions and incorporating, or possibly substituting, standardized solutions focused on increased harvesting with approaches that target individual traits, especially behavioural characteristics. However, management practices are deeply rooted in socio-cultural and economic contexts, making it challenging to implement scientific recommendations based solely on the ecology of the species.

## Compliance with ethical standards and legal requirements

Legal requirements and ethical guidelines for the care and use of animals were strictly adhered to. The research complied with the ARRIVE, ASAB/ABS, French National Charter on the Ethics of Animal Experimentation guidelines, and the standards of good practice established by Costello et al. (2016). It was carried out in accordance with the EU Directive 2010/63/EU for the protection of animals used for scientific purposes, its transposition into French law (French Rural Code, Art
R214-87 to R214-137) and the specific environmental legislation (French Environmental Code, Book IV). Appropriate permits (33-2019-07-23-002 and 33-2020-09-03-004 Permits Numbers) and landowners' consents were obtained for access to the animals. All procedures involving animals were carried out by operators with extensive experience in capturing and handling large wild mammals and were performed under the supervision of qualified staff in the design of research projects involving free-ranging wild animals.

## CRediT authorship contribution statement

**Carole MARIN:** Writing – review & editing, Writing – original draft, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Jérôme WERNO:** Writing – review & editing, Resources, Project administration, Methodology, Investigation, Funding acquisition. **Grégoire LE CAMPION:** Writing – review & editing, Validation, Formal analysis. **Laurent COUDERCHET:** Writing – review & editing, Supervision, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

Data will be made available on request.

## Acknowledgements

We thank the staff of the Hunting Federation of Gironde for their fieldwork, Bordeaux Metropolis hunters, wolf-hunters and Bruges Natural Reserve staff for their help with trapping wild boar. Equipment and field operations were funded by a grant from the Nouvelle-Aquitaine Region and made available to the Regional Hunting Federation. Scientific support was the subject of an agreement between the French National Centre for Scientific Research (CNRS 5319) and the Regional Hunting Federation. Conceptualization and analyses were carried out as part of a PhD study fully funded by the French Ministry of Research. No funding, grants or other support was received for the preparation of this manuscript.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2024.176436.

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