Hunting and human disturbance affect red deer activity more than natural predators in a human-dominated landscape

Hunting and human disturbance affect red deer activity more than natural predators in a human-dominated landscape | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 30 October 2025 V2 Latest version Share on Hunting and human disturbance affect red deer activity more than natural predators in a human-dominated landscape Authors : Martin Boer-Cueva 0009-0001-8731-9430 , Giulia Bombieri 0000-0002-0374-1985 , Emma Centomo 0009-0008-4245-3581 , Piergiovanni Partel , Enrico Dorigatti , Enrico Ferraro , Ilaria Greco , Francesco Rovero 0000-0001-6688-1494 , and Marco Salvatori 0000-0001-5491-4797 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176131097.78548618/v2 Published Ecology and Evolution Version of record Peer review timeline 764 views 193 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Across Europe, landscapes where large carnivores, large herbivores, and human communities coexist are expanding, reflecting the widespread recovery of large mammal populations in recent decades. The influence of top-down effects of wolves on large herbivores has been extensively studied in areas with relatively little anthropogenic disturbance, but less is known about their effect in human-dominated landscapes. We systematically collected camera-trap data over five consecutive autumn hunting seasons in an area of the eastern Alps which is intensely frequented by tourists and trekkers, and partially open to ungulate hunting. We used a quasi-experimental design, with half of the sampling sites located within non-hunting areas and half outside. Applying generalised additive mixed models (GAMMs) with cyclic cubic splines we investigated the effect of wolf, as well as lethal (hunting) and non-lethal (recreational) human activities on red deer spatio-temporal activity pattern. Similarly, we analysed the effect of recreational activities and red deer site-use on the spatio-temporal activity pattern of wolves. Hunting and outdoor recreation were associated with overall lower red deer activity, as well as reduced dawn-dusk peaks and increased nocturnality, respectively. Interestingly, wolf site-use did not have a significant effect on red deer activity level nor did it appear to markedly impact the shape of their temporal curve. Wolves were markedly more active in areas highly used by red deer, and remained strongly nocturnal even where human activity was scarce. Our results show that humans, through both lethal and non-lethal activities, elicit stronger responses in red deer than their natural predator. Behavioural constraints imposed by humans on red deer, coupled with the cursorial predatory strategy of wolves, likely limit the possibility of wolf avoidance by red deer. In human-dominated European landscapes, human disturbance can therefore override natural predator-prey dynamics, reshaping behavioural landscapes and potentially increasing predator and prey spatio-temporal co-occurrence. Introduction Despite the high human population density and the pervasive presence of infrastructure and urban areas, European landscapes have recently witnessed an increase in population size and range of many large mammals (Passoni et al., 2024). Rural land abandonment and more sustainable harvesting have allowed the recovery of large herbivores, which in turn have facilitated the return of large carnivores, re-establishing predator-prey relationships after centuries of absence in many European areas (Linnell et al., 2020; Chapron et al., 2014). Although the effects of large carnivores on their prey have been widely studied in relatively undisturbed ecosystems in North America (Gompper 2002; Hebblewhite et al., 2005; Levi and Wilmers 2012), their relative importance compared to human influences —such as recreational hunting and outdoor activities— remains poorly understood in Europe’s human-dominated landscapes. In these systems, high levels of anthropogenic disturbance coexist with both large herbivores and their predators, and emerging evidence suggests that human pressures may modulate or even weaken the ecological effects of large carnivores (Kuijper et al. 2016; van Beeck Calkoen et al., 2023). The overwhelming effect of humans on the behaviour of wildlife has been conceptualised through the ‘human super-predator’ effect, whereby humans elicit the greatest behavioural change in both prey and predator species (Darimont et al. 2015; Clinchy et al., 2016; Crawford et al., 2022; Zanette et al., 2023). The pressure of hunting activities has been shown to outweigh that of natural predators in shaping large herbivores’ behaviour (Ciuti et al., 2012), evoking proactive and reactive anti-predator responses in game species (Ikeda & Koizumi, 2024; Roekel et al., 2024; Vanderlocht et al., 2025a). In red deer ( Cervus elaphus ), selection for safer areas with dense forest cover has been observed to increase during the hunting season, with a corresponding decrease in avoidance of riskier areas during nighttime, when hunting does not occur (Vanderlocht et al., 2025a). Additionally, non-lethal anthropogenic activities, such as outdoor recreation, can induce spatio-temporal avoidance behaviours in large mammals, a phenomenon of increasing conservation management relevance as nature-based tourism is rapidly gaining popularity worldwide (Balmford et al., 2015; Larson et al., 2016; Salvatori et al., 2024). Exposure and connection with nature have a proven positive effect on human wellbeing (Capaldi et al., 2015), and while they can foster positive attitudes towards conservation (Richardson et al., 2020), they can also generate adverse ecological effects, modifying wildlife assemblages, species distribution and behaviour (Reed and Merenlender 2008; Sytsma et al., 2022). Several studies reported a negative relationship between ungulates and human recreation; for example, in the USA, elk (Cervus canadensis ) and moose ( Alces alces) both decreased their site use intensity or feeding time in areas with higher human presence (Ciuti et al., 2012; Anderson, Waller and Thornton 2023). These human-avoidance behaviours essentially decrease the amount of available suitable habitat (Gaynor et al., 2025; Smith et al., 2024) and constrain the timing of activity (Clinchy et al., 2016; Suraci et al., 2019; Oberosler et al., 2017), despite some evidence showing considerable plasticity and resilience of large herbivores to human disturbance (Salvatori et al., 2023). Both correlation and experimental studies have demonstrated that large predators also fear humans and adopt avoidance behaviours similar to those observed in other wildlife species (Gaynor et al. 2018; Ordiz et al. 2021; Bryan et al. 2015; Smith et al. 2017; Kasper et al. 2025). Overall, lethal human activities such as hunting and non-lethal activities such as outdoor recreation may compound, hindering responses of ungulates towards natural predators, such as temporal activity shifts (Bonnot et al., 2020; Vanderlocht et al., 2025b), forcing prey and predator spatio-temporal co-occurrence (Patten et al., 2019). However, distinguishing the effects of these two types of human activities remains difficult, as most studies have considered only one activity type rather than comparing them directly (Kumar et al., 2023; Liu et al., 2023). In areas where humans hunt, and where outdoor activities and large predators co-occur, large herbivores must cope with a multi-faceted landscape of fear, with multiple and potentially contrasting predatory pressures (Bonnot et al., 2020; Lone et al., 2014). Natural predation by wolves ( Canis lupus ) has been shown to engender spatial avoidance of riskier areas by red deer (Kuijper et al., 2015), with less time spent foraging in areas with wolf scent marking (van Beeck Calkoen et al., 2021). More broadly, studies have also reported increases in vigilance, reduced foraging (Creel et al., 2005; Kuijper et al., 2014; Weterings et al., 2022) and temporal activity shifts: for example, Vanderlocht et al. (2025b) found that red deer had a long-lasting increase in diurnality in response to the recolonisation by wolves. In the Alps, red deer usually constitute the main food item in wolf diet (Gazzola et al., 2005), underscoring the tight predator-prey interaction that links these two species and the potential for a landscape-of-wolf-fear affecting red deer behaviour. However, Gerber et al., (2024) highlight that the influence of wolves on ungulates in human-dominated landscapes remains poorly understood. Furthermore, in cases when prey species perceive humans as a lower threat relative to their natural predators, they may exploit human presence or periods of high human activity to reduce their risk of predation by natural predators — a concept known as the human shield hypothesis (HSH) (Berger, 2007; Muhly et al., 2011; Gaynor et al., 2025). Although the HSH has been studied and analysed across many ecosystems and species, it appears to be a rare and highly case-dependent phenomenon, often incorrectly claimed (Gaynor et al., 2025). The rise of both the human super predator and HSH in the ecological literature poses an interesting yet conceptually challenging question, as it may be difficult to predict how species respond to human presence. In this study, we coupled systematic camera-trapping with a quasi-experimental design to assess the spatio-temporal responses of red deer to hunting, recreational human activity, and grey wolves during five consecutive years during late summer - autumn in a human-dominated area of the eastern Alps. The season chosen corresponded to the ungulate hunting season and a period of high mobility of wolves, with cubs of the year having left the randez-vous sites. We hypothesised spatial avoidance by red deer of both human-related and wolf-related risk, and thus predicted that red deer activity level would decrease in relation to greater human activities (Prediction P1), higher wolf activity (Prediction P2) and at sites where hunting is permitted (Prediction P3). However, we hypothesised that these drivers would exert opposite pressures on deer activity timing (Figure 1): human activities and hunting would push red deer towards nocturnality (Predictions P4 and P5), whereas wolves would drive ungulate behaviour towards diurnality (Prediction P6; Figure 1). In accordance with the human super-predator hypothesis, we predicted that the activity level and activity pattern of wolves would also be influenced by human disturbance, with an expected reduction in activity level (Prediction P7) and an increase in nocturnality (Prediction P8). Figure 1. Schematic diagram of predicted effects of human outdoor activities, hunting and wolves on red deer diel activity levels in the study area, eastern Italian Alps. Yellow arrows indicate the hypothesised effect of increased diurnality of red deer in response to wolves, while the blue arrows show the hypothesised effect of increased nocturnality of red deer in response to hunting and outdoor activity. The net effect of these opposing drivers will depend on the relative perceived risk of humans and wolves by red deer. The shift from blue (nighttime) to yellow (daylight) background is centred on the sunrise and sunset times for mid-September in the study area. Material and methods Study area Our study was conducted in the protected area Parco Naturale Paneveggio Pale di San Martino and adjacent areas (46° 120 N, 11° 480 E) in the Dolomites, eastern Italian Alps (Figure 2). The park and its surroundings are characterised by being a mountainous, alpine area – with elevation ranging from approximately 600 to 3,192 m a.s.l. The vegetation consists of predominantly coniferous species at higher elevations (e.g., Picea abies , Pinus cembra and Larix decidua ), while at lower elevations, beech and ash forests are more common ( Fagus sylvatica, Fraxinus excelsior, Abies alba). The area is a popular tourist destination for outdoor recreation, with many tourist facilities, forestry roads, hiking and mountain biking trails and the presence of logging activities (see Salvatori et al., 2024). Within the park, forestry roads are only accessible to vehicles of forestry personnel and residents, including hunters. Within its boundaries, the park includes state forest reserves where hunting is not allowed, whereas ungulate hunting regularly occurs in the rest of the protected area. The ungulate hunting season in the study area lasts from the first sunday of September to December 31th of each year, with the exception of Tuesdays and Fridays, and hunting activities are allowed from one hour before sunrise to one hour after sunset, i.e., between 04:48 and 19:27 local solar time. Red deer are usually hunted from vantage points with good visibility, but can be shot also during walking hunts, with a maximum shot distance allowed of 400 m. Red deer are subject to selective hunting, in which the hunting quota is established based on the census of the previous year, with a balanced proportion of young and adults as well as males and females. Since 2018, 1 to 3 wolf packs have inhabited the study area, potentially re-establishing natural predation patterns after centuries of absence. Figure 2. Map of the study area in the eastern Italian Alps. Purple squares and yellow circles show the locations of camera traps for sites that permit or do not permit hunting, respectively. The orange line indicates the border of the protected area Paneveggio Pale di San Martino Nature Park. Base map satellite imagery: ESRI world imagery (ESRI Satellite https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/%7bz%7d/%7by%7d/%7bx%7d ). Data collection and management Between 2020 and 2024, we sampled through systematic camera-trapping 30 sites along hiking trails and forestry roads. Trail-based sampling was chosen to increase the detection probability of elusive carnivore species and at the same time to quantify human trail use (Greco et al., 2025). Trails and forestry roads are expected to be the features where human-related and wolf-related risks reach their maxima, providing an optimal setting to test the responses of red deer to hunters, outdoor recreation and wolves. Sampling sites were positioned following a regular grid with 2X2 km cells, within forest environments and excluding areas above the tree line (approximately 1,800 m a.s.l.), as close as possible to the centroid of each cell. Camera traps were positioned on trees at about 60 cm above the ground and a distance of 3–5 m from the target trail or forestry road, without the use of bait. We systematically sampled the 30 sites across the five years, maintaining the same positions, except for small scale adjustments (e.g., if the tree was cut, a nearby tree was chosen). The sampling took place each year between early September to early October, during the ungulate hunting season, for a total of 4,793 trapping days (average sampling duration per site: 31.95 days). Half of the sampled sites were located within state forest reserves, which do not permit any hunting activity, while the other half were in areas that permit hunting, providing a quasi-experimental design to test the effect of the late summer-early autumn hunting season on the activity of red deer (Figure 2). Once the cameras were retrieved, the photographs were processed using the specialised software Wild.AI (Niccoli et al., 2025), hosted on a platform maintained by the University of Florence, which supports photo storage, organisation, and identification. Image classification was initially performed through the program’s built-in detector, which grouped them into four categories: “Blank”, “Humans”, “Vehicles”, and “Wildlife”. Those assigned to the “Wildlife” category were subsequently manually identified to species level. For the purpose of this study, we extracted only data including humans, vehicles, red deer and wolves from the full dataset. Data analysis We used the method developed by Iannarilli et al. (2024) to study the activity pattern of the target species in relation to covariates (i.e., predator/prey, outdoor activities and hunting). Thus, we first subdivided red deer events into 48 daily intervals of 30 minutes, and modelled them through generalised additive mixed models (GAMMs) with cyclic cubic splines. We related red deer activity level and shape to the number of humans ( humans ), vehicles ( vehicles ) and wolf sequences ( wolves ) and to a categorical variable indicating whether each site was open or closed to hunting activities ( hunting ). Human sequences included pedestrians and cyclists, while vehicles included all types of motorised vehicles. Sequences were defined as observations occurring at intervals of at least 15 minutes to ensure independence, and were corrected by sampling effort subdividing by the number of days each camera was active during each year and multiplying by 100. The number of wolf sequences was corrected for wolf group size to account for the potentially stronger effect of the sequences in which wolves were more numerous (see Ferretti et al., 2023). For example, we considered a sequence with three wolves as equivalent to three sequences with a single wolf. We henceforth refer to this variable as ‘individual wolf events’. To account for random variations across sites and years, we also considered site- and year-random intercepts. For the cyclic cubic splines, we used a number of basis functions k = 24, i.e. half the number of the half-hourly bins, placing therefore one knot for every hour. Site-to-site variability in activity pattern can be decomposed into the variability in the frequency of site use, or activity level (i.e., how much a site is used), and in the timing of site use (i.e., when a site is used). When analyzing animal activity patterns with GAMMs with cyclic cubic splines in relation to spatial covariates, the variability in activity level can be modelled through linear terms that control the overall vertical position of the activity curve on the y axis. The variability in activity timing (i.e., the shape of the activity curve) is instead modelled through smoother terms that are allowed to vary by the spatial covariate of interest (Iannarilli et al., 2024). We tested the potential effect of the four variables on both red deer activity level and timing, including them both as linear and smoother terms, respectively. Numerical covariates were standardised, subtracting the mean and dividing by the standard deviation, to make regression coefficients directly comparable and to ease model convergence. We formulated a full model with the following structure: red_deer_events ~ humans + vehicles + hunting + wolves + s(time) + s(time*humans) + s(time*vehicles) + s(time*hunting) + s(time*wolves)+ 1|site + 1|year And compared it with alternative simpler models with 3, 2, 1 and no covariate, ranking them according to AIC (Burnham and Anderson, 2004). When multiple models had a ΔAIC < 2 we model-averaged their predictions using their AIC weight. We followed a similar approach to assess wolf activity pattern, this time including the number of humans ( humans ), vehicles ( vehicles ) and red deer sequences ( red_deer ) as covariates, but not hunting, which was highly correlated with the number of red deer sequences (see Results). wolf_events ~ humans + vehicles + red_deer + s(time)+ s(time*humans) + s(time*vehicles) + s(time*red_deer)+ 1|site + 1|year All models were run through the bam function of the mgcv package (Wood, 2011) in the Rstudio environment (RStudio Team, 2020), with R version 4.3.1. (R Core Team, 2023). Results We collected 10,454 sequences of humans (mean sequences per site and year = 97.65±112.53 SD), 4,096 sequences of vehicles (33.47±76.41), 1,687 sequences of red deer (11.32±14.41), and 221 individual events of wolves (1.48±3.74; Table S1), across the five sampling sessions and 30 sites. Wolf group size across the years ranged between 1 and 12 individuals (mean = 2.27±1.78 SD). Model selection for red deer indicated the model with hunting , humans and wolves as the best one in terms of AIC score, with the second ranked model having a Δ AIC > 2 (Table 1; Table S2). The first ranked model explained 47.1% of the deviance and showed a negative effect of hunting and human activities on red deer activity level ( βhuntingYES = -1.28 ± 0.46 SE; βhumans = -0.45 ± 0.10; Figure 3A and 3B), but no relevant effect of individual wolf events ( βwolf = 0.01 ± 0.07; Figure 3C). All three variables had a significant effect on the shape of red deer activity, with hunting and human activities mediating major changes in the activity curve, whereas the effect of wolves was minimal (Figure 3). Table 1. Model selection table for red deer activity pattern derived from systematic camera-trapping in the eastern Italian Alps. Models are ranked based on AIC values. model deviance logLik AIC AICweight ΔAIC hunting + humans + wolves + Time + 1|Site + 1|Year 3561.07 -2956.51 6045.49 0.78 0.00 hunting + humans + vehicles + wolves + Time + 1|Site + 1|Year 3560.47 -2956.21 6047.97 0.22 2.48 humans + wolves + Time + 1|Site + 1|Year 3582.15 -2967.05 6062.03 0.00 16.54 hunting + humans + Time + 1|Site + 1|Year 3600.12 -2976.03 6067.75 0.00 22.26 humans + vehicles + wolves + Time + 1|Site + 1|Year 3585.35 -2968.64 6070.95 0.00 25.46 hunting + humans + vehicles + Time + 1|Site + 1|Year 3597.85 -2974.89 6073.67 0.00 28.18 hunting + vehicles + wolves + Time + 1|Site + 1|Year 3608.56 -2980.25 6079.78 0.00 34.29 hunting + wolves + Time + 1|Site + 1|Year 3613.65 -2982.80 6082.89 0.00 37.40 humans + vehicles + Time + 1|Site + 1|Year 3623.14 -2987.54 6091.48 0.00 45.99 humans + Time + 1|Site + 1|Year 3631.50 -2991.72 6092.28 0.00 46.79 vehicles + wolves + Time + 1|Site + 1|Year 3629.33 -2990.63 6094.20 0.00 48.71 wolves + Time + 1|Site + 1|Year 3634.35 -2993.15 6097.21 0.00 51.72 hunting + vehicles + Time + 1|Site + 1|Year 3645.08 -2998.51 6104.56 0.00 59.07 hunting + Time + 1|Site + 1|Year 3653.38 -3002.66 6105.40 0.00 59.91 vehicles + Time + 1|Site + 1|Year 3666.05 -3009.00 6119.29 0.00 73.80 Time + 1|Site + 1|Year 3678.04 -3014.99 6123.42 0.00 77.93 Time + 1|Site 3768.27 -3060.11 6205.86 0.00 160.37 Time 5342.15 -3847.04 7723.63 0.00 1678.14 Figure 3. Predictions of red deer activity pattern in relation to the covariates included in the best GAMM model: hunting (Panel A), human activities (Panel B) and wolf activity (Panel C). The light grey background indicates nocturnal hours, falling between mean sunset and sunrise time. For human and wolf activity, the low and high levels were defined as the 2.5 and 97.5 percentiles of the covariate, keeping the other covariate at its mean value. Data derive from systematic camera-trapping in the eastern Italian Alps. Model selection identified red deer and human sequences as the best predictors of wolf activity patterns, although the model with red deer as the only predictor was within 2 ΔAIC units ( Table 2). The variable humans had a variable importance weight of 0.49, indicating weak support. The first-ranked model explained 38.2% of the deviance and showed a positive effect of red deer events on wolf activity level ( βred_deer = 0.43 ± 0.13), but no relevant effect of red deer on the shape of wolf activity, which remained markedly nocturnal (Figure 4). Human sequences had no relevant effect on wolf activity level ( βhumans = 0.08 ± 0.20) nor timing (Table S3). Since two models had a ΔAIC < 2, we model averaged predictions using AIC weights to obtain the results shown in Figure 4. Table 2. Model selection table for wolf activity pattern derived from systematic camera-trapping in the eastern Italian Alps. Models were ranked based on AIC values. model deviance logLik AIC AICweight ΔAIC humans + red_deer + Time + 1|Site + 1|Year 781.35 -533.33 1137.65 0.46 0.00 red_deer + Time + 1|Site + 1|Year 783.46 -534.39 1137.71 0.44 0.06 vehicles + red_deer + Time + 1|Site + 1|Year 780.59 -532.95 1142.00 0.05 4.35 humans + vehicles + red_deer + Time + 1|Site + 1|Year 778.31 -531.81 1142.90 0.03 5.25 Time + 1|Site + 1|Year 795.21 -540.26 1145.40 0.01 7.75 humans + 1|Site + 1|Year 792.13 -538.72 1147.44 0.00 9.79 vehicles + 1|Site + 1|Year 790.64 -537.98 1148.22 0.00 10.57 humans + vehicles + 1|Site + 1|Year 788.06 -536.69 1150.70 0.00 13.05 Time + 1|Site 821.43 -553.37 1164.05 0.00 26.40 Time 1103.61 -694.46 1401.09 0.00 263.44 Figure 4. Model-averaged predictions of wolf activity pattern in relation to red deer and human activity from the two GAMM models with ΔAIC<2. The light grey background indicates nocturnal hours, falling between mean sunset and sunrise time. Low and high levels of the covariates were defined as the 2.5 and 97.5 percentiles, keeping the other covariate at its mean. Data derive from systematic camera-trapping in the eastern Italian Alps. Discussion Through a quasi-experimental design including 15 hunted and 15 control sites sampled across five years, combined with an integrated spatio-temporal statistical approach, we found that hunting was associated with reduced overall activity levels and a marked decrease of dawn-dusk peaks in red deer. Human recreational activities were also a relevant predictor of red deer activity levels and timing, with lower overall activity and higher nocturnality at sites with intense outdoor recreation. Meanwhile, wolves did not affect the activity levels of red deer, nor did they appear to have a marked effect on the shape of their temporal curve. Contrary to our predictions, wolf activity levels were positively related to red deer site-use intensity but were not affected by human activities, maintaining a strictly nocturnal pattern at both high and low human presence. Our findings suggest that within human-dominated landscapes, humans may elicit a greater behavioural response in ungulates than their natural predators – in this case, wolves - both through consumptive and non-consumptive activities. We found that red deer did not markedly shift their activity peaks in response to wolf presence, rejecting our Prediction P6 (temporal shift in response to wolf). This is in accordance with Vanderlocht et al. (2025b), that observed a shift towards more diurnal activity by red deer in response to wolf recolonisation only in areas where hunting was not allowed: in the presence of recreational hunting, the increased diurnality disappeared, indicating that hunting can inhibit temporal anti-predator responses. This pattern has also been observed in other European ungulates: Bonnot et al., (2020) showed that in areas across Europe where both hunting and Eurasian lynx ( Lynx lynx) were present, roe deer ( Capreolus capreolus ) became increasingly nocturnal, despite marked lynx nocturnality, suggesting that humans were perceived as a higher risk than natural predators. Similarly, roe deer in the southwestern Alps actively selected areas with high wolf density during hunting periods, indicating a trade-off between human hunters and natural predators (Ruco and Marucco 2025). The rate at which humans hunt other species is several orders of magnitude higher than natural terrestrial or marine predators, making them exceptionally lethal ‘super-predators’ (Darimont et al., 2015). For these reasons, when prey species encounter human cues, they are more likely to react to avoid immediate mortality, which may explain the spatial and temporal avoidance of primarily lethal humans (hunters), but also non-lethal humans (recreational) (Smith et al., 2024; Trimmer et al., 2017). Indeed we observed that the level of activity of red deer at sites open to hunting was less than half that of sites where hunting did not occur (Prediction P3 - lower activity in presence of hunting - confirmed), with particularly strong reduction of the dawn and dusk activity peaks (Prediction P5 -temporal shift in response to hunting - confirmed), suggesting a strong spatial, and to a lesser extent temporal avoidance. This matches previous evidence on deer proactive avoidance of hunting activities: for example, sika deer ( Cervus nippon) in Japan displayed spatial avoidance of areas with high hunting pressure and were more nocturnal in hunting areas compared to areas where hunting was not permitted (Ikeda & Koizumi, 2024). Even though to a minor degree than hunting, outdoor recreation resulted in lower red deer activity, and led to a relatively lower diurnality, confirming prediction P1 and P4 (lower activity and temporal shift in response to outdoor activity, respectively) and results from a previous study targeting several areas (Salvatori et al., 2024). Overall, these human avoidance responses may be effective in reducing mortality, yet they also entail a substantial physiological cost, altering natural activity patterns, such as foraging, and potentially shaping broader community dynamics. For example, spatial avoidance of humans may result in clustered hotspots of forest browsing, with alterations of the carbon and nitrogen cycling (Di Nicola et al., 2023; Segar et al., 2023; Donini et al., 2024). It is important to note, however, that our sampling was concentrated along trails, where human presence is disproportionately high compared to the wider landscape. This may have biased our results by over-representing the risk of human–wildlife encounters, potentially exaggerating the strength of human effects on red deer activity patterns. However, trails are also heavily exploited by wolves for faster and more efficient movements (Dickie et al., 2017; Greco et al., 2025), therefore, they are landscape features where both human-related and wolf-related risks should be near their maximum. In addition, Greco et al. (2025) found that the temporal activity curve of red deer exhibited no substantial variation between trail-based and random-based sampling, despite a slightly higher nocturnality recorded with the former. Interestingly, our prediction P2 (lower red deer activity in response to wolves) was rejected, as red deer activity level was not affected by the site use intensity of wolves. In contrast, we observed a strong positive relationship between wolf activity rate and red deer site use intensity, suggesting that in our study system, wolves might be favoured in the ‘predator-prey race’, effectively targeting areas and time of day more used by red deer. As highlighted by Smith et al. (2019), the outcome of the behavioural race between prey and predator is highly dependent on which of the two players is more constrained in terms of access to forage and refuge, and by predator hunting mode. Cursorial predators that actively chase prey are expected to evoke weaker behavioural responses in prey compared to ambush, sit-and-wait predators (Preisser et al. 2007). The landscape of fear shaped by wolves, as cursorial predators and habitat generalists, is indeed highly dynamic (Kohl et al., 2018) and might be significantly less spatially predictable than human-mediated risk. In our study area, hunting never occurs within state forest reserves, and human disturbance is concentrated along hiking trails and forestry roads, making both lethal and non-lethal human activities predictable and proactively avoidable compared to natural predators. In contrast to red deer, wolf activity levels did not change according to the intensity of site use by humans, rejecting Prediction P7 (lower wolf activity in response to humans). This highlights the plasticity of wolves in adjusting to anthropogenic disturbances, explaining their recent success in recolonising European areas from where they were previously eradicated. However, we observed that wolves remained strictly nocturnal regardless of low or high human activity, suggesting that wolves ensure stable human avoidance by temporal, rather than spatial segregation (Sunde et al., 2024). Prediction P8 - increased wolf nocturnality in response to humans - was therefore rejected. At a broader scale, studies that assessed wolf activity patterns across a gradient of human disturbance have noted that wolves are more diurnal in areas with low or restricted human presence (Ferreiro-Arias et al., 2024; Mancinelli et al., 2019; Martínez-Abraín et al., 2023), indicating the pronounced nocturnality we observed as a specific coping mechanism of wolves to human-dominated landscapes. The observed overlap between red deer and wolf activity patterns therefore is in line with the human super-predator hypothesis. However, a similar pattern could emerge if wolves do not fear humans directly but instead adjust their activity to track prey that have become more nocturnal in response to human pressure, as may occur where wolves are protected while ungulates are harvested (Kasper et al., 2025). These mechanisms are not mutually exclusive, and could act simultaneously to shape predator-prey interactions. Overall, our results do not support the human shield hypothesis, as expected in areas where large herbivores are hunted; rather, they show that red deer tend to prioritise the avoidance of lethal and non-lethal human-driven risks over risk from natural predators. This confirms the conclusions of Gaynor et al. (2025) on HSH being a rare and case-specific phenomenon, and corroborates findings of Patten et al. (2019) on the increased predator-prey co-occurrence caused by human avoidance by both actors. The pre-eminent role of humans in shaping large herbivore behaviour, a signature of the Anthropocene, might therefore undermine the potential for predator-driven, behaviourally-mediated trophic cascades, weakening the role of large carnivores as top-down ecosystem regulators (van Beeck Calkonen et al., 2023; Allen et al., 2017). The overarching effect of human disturbance can also temporally and spatially ‘squeeze’ prey species, leaving limited opportunity to mitigate risk by natural predators through proactive behavioural adjustments and habitat selection (Crosmary et al., 2012; Vanderlocht et al., 2025b). 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Current Biology , 33 (21), 4689-4696.e4. https://doi.org/10.1016/j.cub.2023.08.089 Information & Authors Information Version history V1 Version 1 24 October 2025 V2 Version 2 30 October 2025 Peer review timeline Published Ecology and Evolution Version of Record 15 Feb 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords human disturbance human-wildlife coexistence hunting outdoor recreation predator-prey interactions wolf Authors Affiliations Martin Boer-Cueva 0009-0001-8731-9430 Autonomous University of Madrid View all articles by this author Giulia Bombieri 0000-0002-0374-1985 Museo delle Scienze View all articles by this author Emma Centomo 0009-0008-4245-3581 Museo delle Scienze View all articles by this author Piergiovanni Partel Parco Naturale paneveggio pale di san martino View all articles by this author Enrico Dorigatti Parco Naturale paneveggio pale di san martino View all articles by this author Enrico Ferraro Associazione cacciatori trentini View all articles by this author Ilaria Greco Università degli Studi di Firenze View all articles by this author Francesco Rovero 0000-0001-6688-1494 Università degli Studi di Firenze View all articles by this author Marco Salvatori 0000-0001-5491-4797 [email protected] Università degli Studi di Firenze Museo delle Scienze View all articles by this author Funding Information Museo delle Scienze Metrics & Citations Metrics Article Usage 764 views 193 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Martin Boer-Cueva, Giulia Bombieri, Emma Centomo, et al. 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