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A human-neutral large carnivore? No patterns in the body mass of gray wolves across a gradient of anthropization

  • Jacopo Cerri ,

    Contributed equally to this work with: Jacopo Cerri, Carmela Musto

    Roles Conceptualization, Formal analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Department of Veterinary Medicine, University of Sassari, Sassari, Italy

  • Carmela Musto ,

    Contributed equally to this work with: Jacopo Cerri, Carmela Musto

    Roles Conceptualization, Data curation, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Department of Veterinary Medical Sciences, University of Bologna, Bologna, Italy

  • Federico M. Stefanini,

    Roles Formal analysis, Methodology, Supervision, Writing – original draft

    Affiliation Dipartimento di Scienze e Politiche Ambientali, Università degli Studi di Milano “La Statale”, Milano, Italy

  • Umberto di Nicola,

    Roles Conceptualization, Data curation, Methodology

    Affiliation Gran Sasso and Monti della Laga National Park, L’Aquila, Italy

  • Nicoletta Riganelli,

    Roles Investigation, Methodology, Project administration, Resources, Writing – original draft

    Affiliation Gran Sasso and Monti della Laga National Park, L’Aquila, Italy

  • Maria C. Fontana,

    Roles Conceptualization, Data curation, Investigation, Methodology, Project administration, Supervision

    Affiliation Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia-Romagna Bruno Ubertino, Brescia, Italy

  • Arianna Rossi,

    Roles Conceptualization, Data curation, Investigation, Methodology, Resources

    Affiliation Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia-Romagna Bruno Ubertino, Brescia, Italy

  • Chiara Garbarino,

    Roles Conceptualization, Data curation, Methodology, Resources, Supervision

    Affiliation Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia-Romagna Bruno Ubertino, Brescia, Italy

  • Giuseppe Merialdi,

    Roles Data curation, Investigation, Methodology, Resources

    Affiliation Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia-Romagna Bruno Ubertino, Brescia, Italy

  • Francesca Ciuti,

    Roles Conceptualization, Data curation, Investigation, Methodology, Supervision, Validation

    Affiliation Canislupus Italia, Firenze, Italy

  • Duccio Berzi,

    Roles Data curation, Investigation, Resources, Supervision, Validation, Writing – original draft

    Affiliation Canislupus Italia, Firenze, Italy

  • Mauro Delogu,

    Roles Data curation, Funding acquisition, Methodology, Supervision, Validation

    Affiliation Department of Veterinary Medical Sciences, University of Bologna, Bologna, Italy

  • Marco Apollonio

    Roles Conceptualization, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing

    Affiliation Department of Veterinary Medicine, University of Sassari, Sassari, Italy


The gray wolf (Canis lupus) expanded its distribution in Europe over the last few decades. To better understand the extent to which wolves could re-occupy their historical range, it is important to test if anthropization can affect their fitness-related traits. After having accounted for ecologically relevant confounders, we assessed how anthropization influenced i) the growth of wolves during their first year of age (n = 53), ii) sexual dimorphism between male and female adult wolves (n = 121), in a sample of individuals that had been found dead in Italy between 1999 and 2021. Wolves in anthropized areas have a smaller overall variation in their body mass, during their first year of age. Because they already have slightly higher body weight at 3–5 months, possibly due to the availability of human-derived food sources. The difference in the body weight of adult females and males slightly increases with anthropization. However, this happens because of an increase in the body mass of males only, possibly due to sex-specific differences in dispersal and/or to “dispersal phenotypes”. Anthropization in Italy does not seem to have any clear, nor large, effect on the body mass of wolves. As body mass is in turn linked to important processes, like survival and reproduction, our findings indicates that wolves could potentially re-occupy most of their historical range in Europe, as anthropized landscapes do not seem to constrain such of an important life-history trait. Wolf management could therefore be needed across vast spatial scales and in anthropized areas prone to social conflicts.


The presence of medium and large carnivores in anthropized environments increased over the last few decades, due to urban sprawl and agricultural development in the Global South [1], and a mix of socio-ecological dynamics and legal protection in the Global North [24]. Therefore, a growing number of studies explored how well these species can adapt to increased levels of human presence, to improve their conservation planning and reduce the risk of conflicts with human activities.

Anthropization was found to have three main effects over medium and large carnivores. In some cases, anthropized environments are sub-optimal, due to decreased prey availability [5] the influence of human activity and artificial nightlight on foraging [6,7], persistent human disturbance [8], the impact of infrastructures on population connectivity and mortality [9,10], disease transmission and competition with domestic dogs [11] and the risk of accidental intoxication [12]. These dynamics can in turn raise metabolic stress [13], and limit reproduction and survival, thus creating source-sink dynamics with undisturbed areas [14].

On other occasions, carnivores prosper in anthropized environments, attaining higher body sizes [1517] and densities [18] than those reported for environments with no human presence. Mostly because of reduced competition [19] and the exploitation of alternative food sources [20,21].

Finally, on some other cases, effects are non-linear: moderate levels of anthropization seem to be advantageous [22], or detrimental [13], compared to natural environments, but these effects reverse as anthropization increases.

The expansion of the gray wolf (Canis lupus) in Italy and Europe calls for further research to better forecast its future trajectory. Between early 1990s and mid 2010s wolves recolonized marginal areas in Europe, due to increased forest cover and rural abandonment [3]. In Italy, wolves were legally protected since 1976 and benefited from recovering forests and ungulates for decades, progressively saturating undisturbed habitat patches [23] and then further expanding into increasingly anthropized ecosystems, recovering most of their historical range. Nowadays wolves occur in peri-urban areas and even the Po plain, one the areas in Europe with the highest human density (above 650 inhabitants/Km2).

This dynamic in Italy was probably facilitated by the capacity of local wolf population to cope with human presence, as well as to use food waste as a substitute for large ungulates, a prey base that had been depleted for decades [24], and possibly also by the availability of feral and stray dogs for reproduction [25]. These conditions were certainly different at some other areas in Europe, where forest recovery was often less marked than in Italy [26], in turn hampering connectivity between wolf populations [27]. And where wolves were less adapted to human presence, by having been segregated in areas with low disturbance and a stable prey base like in Poland [28,29], Estonia [30], Sweden [31] or Finland [32].

Provided that this heterogeneity in long-term selective pressures might have turned into a different capacity of exploiting human-dominated landscapes, the situation between Italy and other countries from Southern, Central and Eastern Europe, some of whom have indeed been colonized by individuals from the Italian peninsula (e.g., France) [27], seems to be temporally lagged, but following a rather similar trajectory. Wolves are progressively colonizing human-dominated landscapes, initially with single dispersing individuals or couples [3335], and then either with source-sink dynamics [36,37] and or with well-established packs [38,39] a process that in Italy has been observed 20–15 years ago. Therefore, understanding the suitability of anthropized areas for wolves in Italy, a country where wolf colonization is at a more advanced stage, could be pivotal to evaluate the extent to which the species could re-occupy its historical range in Europe and to forecast the spatial scale of future mitigation measures or zonation policies [40].

Considered the ecology and behavior of the gray wolf, all the three scenarios are equally plausible. In the first one, anthropized areas could be sub-optimal, because replacing large ungulates with smaller prey [41] might raise the energetic costs of foraging, given limited nutritional benefits and because wolves may suffer from disease transmission from domestic dogs [11]. Alternatively, anthropized areas could be favorable for wolves, which could exploit large amounts of food waste [42]. Finally, wolves could show non-linear response to anthropization, as areas with intermediate level of anthropization could still offer large ungulates together with domestic animals and food waste at the same time.

Considered the ecology of gray wolves, anthropization is likely to act through two mechanisms. In the first year of age, anthropization acts on early-life conditions, like for most mammals [43] affecting growth and in turn survival and the capacity to disperse and reproduce of young wolves. While during their first month wolf pups could be fed with food waste, the role of conventional prey become progressively more important as individuals approach adulthood and hunting strategies are developed [44]. Thus, the extent to which a certain environment is optimal for pups is measured by the strength of the temporal growth in body mass, net the effect of total body length. If the environment provided more food sources, young wolves would grow faster and the linear interaction would therefore be positive, reinforcing the effect of age in days over body mass (positive interaction). On the other hand, if environments were sub-optimal, young wolves would grow slower and the effect of age in days over body mass would be weaker (negative interaction).

From the second year of age, when wolves complete their growth [45,46], anthropization is likely to act through sexual dimorphism. Wolves are moderately dimorphic carnivores, with males weighting around 20% more than females [47]. In wolves males are believed to be larger than females both because they engage more often in intergroup aggressions [48], and because they provide prey to lactating females and pups [49]. In facts, a larger body size is deemed to improve either the hunting proficiency of male wolves [50] and their capacity to kill large prey [51,52]. Considered that in anthropized areas adult wolves could exploit human food waste, not relying uniquely on large prey, we predicted the size of both sexes to increase. However, due to reduced selective pressures for larger body size needed to prey upon large ungulates, we expected the size of adult males to increase less than that of females, leading to a decrease in sexual dimorphism. This study is a first attempt to address these two effects of anthropization, across a gradient of urbanized areas in central Italy and a time span of 22 years.


Study area

The study area encompasses the Emilia-Romagna region, the northern provinces of the Tuscany region, and the Gran Sasso and Monti della Laga National Park, in the Abruzzo region, in Italy (Fig 1).

Fig 1. Map of the study area, representing the human footprint index and provinces covered by data collection (highlighted) in Italy.

The Human Footprint Index was available under a CCBY 4.0 license from Venter et al. (2016, see the references) [76].

In Emilia-Romagna and Tuscany, two contiguous regions, a wolf population of at least 110 packs was estimated between 2012 and 2016 [53,54]. In the 90’s wolves were dived in two distinct sub-populations, one in the Apennine ridge and one in coastal and hilly part of central-southern Tuscany and Latium, which subsequently merged as the species expanded its distribution around 2013 [55]. In the Gran Sasso and Monti della Laga National Park available estimates indicate a population of 11–14 packs [56]. Both areas suffered from wolf-dog hybridization, conflicts with livestock and illegal wolf killing [53,57].

The landscape includes a variety of different ecosystems, ranging from coastal areas characterized by Mediterranean maquis to temperate broad-leaved forests and sub-alpine grasslands in the Apennines. In the study area, the human exodus from marginal rural areas at higher elevations [58], produced a strong gradient of anthropization (Fig 1). The portion of the study area in the Emilia-Romagna and Tuscany regions hosts a population of 8.2 million people, across 45,438 km2 (180 inhabitants/km2), while the Gran Sasso and Monti della Laga park spans across 1500 km2 with a population of 138,669 people (92.5 inhabitants/km2).

The trophic niche of wolves in the study area remained quite broad over the last three decades, being centered on wild/domestic ungulates [5966] which are available throughout the region, as Central Italy hosts among the highest densities of wild ungulates in Europe [24], and key prey such as roe deer (Capreolus capreolus) and wild boar (Sus scrofa) are abundant even in croplands and peri-urban areas [67]. However, urban areas are also rich in human food waste. In Italy, each citizen produces between 46 and 127 kg of food waste on each year [68], and waste collection is characterized by periodical inefficiencies and long collection times, with waste sometimes piling up at garbage bins for days and thus becoming available to wildlife (

Collection of dead wolves and laboratory analyses

Our dataset included 107 wolves from the Emilia-Romagna and Tuscany region, as well as 68 wolves from the Abruzzo region. All these animals were dead and recovered by local authorities between 1999 and 2021. The proportion of males, the age of recovered wolves, the season when they were found, and the number of roadkill were similar between Abruzzo and the Emilia-Romagna/Tuscany region (S1 to S4 Figs). A complete overview about our sample is available in Table 1.

Table 1. Sample overview.

Main attributes of the wolves that were analyzed within this study.

The age of each animal was estimated based on dental development, body size and weight [69], dividing individuals between 1–12 months, 13–24 months or older. Until 24 months, individuals were aged by assuming they were born on the 1st of May [70]. We also recorded total length (from the nose to the junction of the tail), the length of the tarsus and tail, the height of the ear, and chest and neck circumferences.

Out of our sample of 175 wolves, 11 of them had not been genetically tested for hybridization with domestic dogs. Among tested individuals, only 8 wolves were recent hybrids.

As the research did not deal with live animals, but instead with animals that had already been found dead, and then subjected to necropsy, it was not necessary to obtain the approval from the animal research ethics committee of the University of Bologna. Moreover, as data were collected by local authorities across the study area, often on public land, no permission to access the study site was needed.

Individual genotyping and taxonomy of individual

Genetic investigation on carcasses were conducted by Istituto Superiore per la Protezione e la Ricerca Ambientale—ISPRA. A portion of lingual muscular tissue was taken and stored in 95% ethanol to genetically determine the species of the examined canids and to detect a possible presence of genetic hybridization signatures [71,72].

Statistical analyses and hypotheses

Both for growth and dimorphish, the parameter of interest, which we then used as a response variable in statistical modeling, was the body mass of individuals. Body mass is strongly related to body condition, which, in mammals, is often measured as the ratio between body mass and total body length [73,74]. We rather modeled the contribution of body length to body mass through a linear predictor, as a covariate-based approach ensured a higher level of flexibility compared to a ratio between the two measures, for example by allowing for non-linear relationships or heteroskedasticity [75].

Body mass was measured as the weight of dead individuals that had been recovered, in kg. Urbanization was measured by means of the Human Footprint Index (hereinafter HFI), obtained by combining multiple layers about man-made structures from satellite, at the resolution of 1 km [76]. The median HFI was calculated in a buffer with a radius of 6 km around the point. This size corresponded to an area of approx. 113 km2 around the point, reflecting the most recent estimates for the home range of the species reported in Italy [77,78].

We controlled for candidate confounding variables, through the so-called “back door criterion” [79]. A complete overview of candidate confounders, acting through some unobserved mediating variables, is shown in the (S5 Fig), in the form of a Directed Acyclic Graph.

We also controlled for some temporal variables that characterized sampling. The day of the year could have affected the level of urbanization of recovered wolves, because human presence in natural environments, and the probability of recovering wolf carcasses, is higher during summer or during the hunting season, which lasts from autumn and early winter. In adult wolves, these seasons could also be characterized by a higher availability of prey, such as young ungulates, compared to winter and spring. Moreover, our data collection covered 22 years and thus we controlled for the year on which each wolf was found. As the wolf population steadily expanded its distribution in Italy, wolves were forced to disperse more and more in urbanized areas [80], while at the same time they could also have increased their average body condition due to the increased abundance of prey species, such as large ungulates. Moreover, as our data had been collected on two separate geographical blocks, corresponding to the Emilia-Romagna/Tuscany and the Abruzzo region, we controlled for this spatial heterogeneity with a dichotomous covariate.

As we found that wolves in the Emilia-Romagna/Tuscany area had a more heterogeneous total body length, we controlled for this variable also on the conditioned variance of total body mass [81].

Models were fitted through the “brms” R package [82]. After a preliminary exploration of body mass distribution, the response variable was modeled as a Student’s-t distribution which, for wolves of one year of age, was truncated at zero. Explanatory variables were standardized and centered, before being included in the model. For each parameter, we selected a moderately informative prior distribution, corresponding to a Normal distribution with mean equal to zero and a variance of one [83]. Models had 5000 MCMC iterations and a burn-in of 1000 iterations. Model selection was based on a backward approach, starting by the most complex model with a spline term and removing one term per time, then comparing nested models by means of leave-one-out cross validation [84].

We performed two types of sensitivity analyses. First, we tested for “collapsibility”, or the extent to which our interaction terms of interest were susceptible to the removal of confounders, that were deemed redundant by leave-one-out cross. Ideally, the removal of unnecessary confounders should not have changed the posterior distribution of interaction terms that we were interested in. Then, once we identified the best candidate model, we performed a sensitivity analysis, by refitting models with the median HFI, calculated on buffers with a radius between 4 and 16 km. This practice created circles with an area between 12 and 800 km2, which exceeded the whole spectrum of values reported for the core area and the home range of the species in Italy, which attains a maximum of approx. 400 km2 [77].


The best candidate model predicting wolf growth during their first year explained 79.0% of total variability in the body mass. The year when wolves were found was the only confounding variable that was retained. The body mass of young wolves increased throughout their first year of age but, net the effect of body length, the magnitude of this change was rather mild. Moreover, the growth in body mass became further milder as anthropization increased (Table 2, Fig 2).

Fig 2. Interactive effect of anthropization and the age in days of recovered wolves of 1 year of age (n = 53).

Plots correspond to the first (a), second (b), third (c) and fourth (d) quartiles of the distribution of median Human Footprint Index, calculated in a buffer with a 6-km radius around the point where animals were found. Plots (a) to (d) therefore corresponds to increasingly urbanized areas. Variables are standardized and centered.

Table 2. Outputs of the best candidate model for body mass of wolves in their first year of age, and for adult wolves.

Anthropization is calculated on a buffer with a 6km radius around the points where animals had been found.

The best candidate model predicting differences in body mass between adult male and female wolves explained 57.8% of total variability in the response. The best candidate model retained the area where individuals had been found as a confounder, and as a predictor of variability in body mass. The body mass of male wolves showed a mild increase, for increasing levels of anthropization, but the body mass of females did not (Table 2, Fig 3). This decreased predictive accuracy, compared to the model for wolves in their first year of age, probably depended upon the impossibility of correctly aging individuals older than three years, and thus to account for age-related variability in their body mass, which increases until 6–8 years of age [85].

Fig 3. Interaction between anthropization and the sex of recovered adult wolves (n = 121).

Plots correspond to the first (a), second (b), third (c) and fourth (d) quartiles of the distribution of median Human Footprint Index, calculated in a buffer with a 6-km radius around the point where animals were found. Plots (a) to (d) therefore corresponds to increasingly urbanized areas. Variables are standardized and centered.

The removal of redundant confounding variables did not change the interactive effect between anthropization and the age of wolves during their first year, nor the interaction between anthropization and the sexual dimorphism in weight among adult wolves (S7 and S8 Figs, S1 and S2 Tables). Moreover, findings from best candidate models did not change, when calculating anthropization on buffers of different size (Animation S1, S2, available at In both models, the analyses of model residuals indicated linear relationships and the semivariogram did not highlight any residual isotropic spatial correlation (S8 to S13 and S14 to S17 Figs). The analysis of posterior predictive checks indicated that the Student’s-t distribution was a suitable approximation for the response variable, and the posterior distribution of model parameters indicated model convergence.


To the best of our knowledge, this research was among the first ones [22] assessing the impact of anthropization over the body condition of a large carnivore, the gray wolf, which is occupying increasingly anthropized landscapes in Italy and Europe. While other studies proved that the gray wolf can exploit foods resources characterizing anthropized environments [41,42], we assessed if these environments can affect the body condition of individuals. Our analyses focused on responses measured at the landscape scale, and we did not quantify resource selection or the effect of time-varying resource availability.

This question is non-trivial, because the increased availability of alternative food sources, characterizing anthropized environments, does not automatically translate into an increased body condition of large carnivores. While food waste is abundant and rather predictable in space, thus decreasing foraging costs [86], its nutritional quality might be suboptimal, as it includes a high proportion of carbohydrates which are not processed by wolves which, compared to domestic dogs, are lacking alpha-amylase [87] and have different gut microbiotas [88]. Moreover, foraging in urban environments is influenced by human disturbance. For example, multiple studies reported negative physiological responses of birds and mammals to human disturbance, like increased oxidative stress or inflammation [89]. As for large carnivores, available evidence indicates that avoidance of humans leads pumas (Puma concolor) living in anthropized landscapes, to engage in energetically expensive, and inefficient, movement behavior [90].

Our findings provide preliminary evidence that wolves, at least in Italy, are capable to cope with anthropized landscapes in a way which is different from what has been reported for other large carnivores: anthropized landscapes seem not to have any clear effect on the body mass of individuals, an important life-history trait. Or, most likely, anthropized landscapes do not show any clear tradeoff between negative and positive effects.

As we adopted a Bayesian framework for inference, we did not use p-values [91]. Instead, our considerations about effects being significant were based on their magnitude, which in our case largely overlapped with zero.

The body mass of young wolves in our sample grew throughout their first year of age, net the effect of an increase in total body length. This reflected an increase in their muscular mass and fat, two components of body condition. However, this growth had only a very mild interaction with anthropization (Fig 2). More anthropized areas had young wolves experiencing a smaller variation in their body mass during their first year, but which have higher body masses at 3–5 months of age. This suggests a positive influence of human derived food sources in the first months of life, i.e. when pup rising is entirely dependent from adults provisioning. This pattern does not provide any clear evidence on whether anthropized environments are optimal or suboptimal for young wolves in their first year of life. Perhaps anthropized environments can provide an abundance of food resources which could be exploited by young wolves during their first weeks, but less prey such as large ungulates which may be better to sustain individuals at 10–12 months. These differences could also be explained with a different size of wolf packs. Anecdotical evidence indicates that anthropized environments in Italy might have smaller packs often represented by a pair with pups. This strategy can be favorable in areas with fewer large prey [92] and a rich disposal of small food items, represented by garbage and domestic pets, but might not pay out well in most of our study area, characterized by high densities of ungulates [24].

The effect of anthropization over sexual dimorphism in adult wolves is also partially unclear. While we expected dimorphism to decrease, we instead found a mild increase, as areas became more anthropized, in the body mass of adult males (Fig 3). Most interestingly, the increase in weight involved only male wolves, while females did not increase in body mass at all, despite urbanized areas should be favorable to both sexes. Even if in large mammals it was shown that food shortage/abundance may constrain/favor the growth of males and females differentially (e.g., wild ungulates) [93], the lack of any increase in the weight of females, under potentially favorable conditions, opens the way to n another explanation. In wolves, dispersal seems to be more common for males [53,94] and in mammals, individuals who disperse usually have a larger size, known as “dispersal phenotype” [95]. Considered the progressive saturation of undisturbed habitats by wolf packs [23,80], the pool of dispersing individuals, could have been constituted preferably by large males, which would have dispersed towards more anthropized areas and they could have entered our sample.

Assessing the effect of anthropization on the body mass of wolves is also an urgent question. Body condition is strongly associated with reproduction [96] and survival in mammals, two demographic parameters that are paramount for the long-term viability of populations. Considering the rapid expansion of the gray wolf in Europe, if anthropized environments do not have any effect on the body mass of individuals, as suggested by our study, this could mean that soon wolves could colonize, reproduce, and survive in a significant portion of their historical range in Europe despite a consistent recent urbanization. Thus, policies for co-existing with them, such as zonation or mitigation measures [40], will be needed across vast spatial scales and their implementation might generate a widespread social debate, as it might go beyond rural areas [97].

Considered the potential impacts of a widespread wolf presence in anthropized landscapes of Europe, our findings urgently call for replication studies, addressing two main points. First, studies should replicate our analyses in other geographical areas. Wild ungulates are core prey for wolves and our study covers some of the areas in Europe with the highest densities of ungulates, whose populations increased over the last two decades and which became widespread even in urbanized settings [98]. Other European countries faced a decrease in the wild boar, a key prey, due to the African Swine Fever [99], have high numbers of unprotected livestock and a different amount of waste in the environment. Thus, in these areas, the impact of anthropization over the body mass of wolves can be different.

At the same time, we also emphasize the need for studies based on much larger samples. While we found no significant effect of urbanization on the growth and sexual dimorphism of wolves, considered our sample size we cannot rule out that an effect with a low magnitude exists statistical power increases with sample size and nuanced interactions can be reliably quantified only by analyzing thousands of individuals. In this study we did not perform power analysis, because we had no prior knowledge about effect size and because we aimed to provide only preliminary evidence. However, if other studies will adopt similar sample sizes, there will be no significant advancement in terms of statistical power and it will be impossible to capture small environmental effects, which could nevertheless be potentially important in the long term. Moreover, large-scale data sharing initiatives are needed also to address two other limitations of this study. First, the gray wolf is distributed across the entire Palearctic, inhabiting a variety of different ecosystems, characterized by different prey assemblages, as well as different interactions with humans. It is therefore possible that in these contexts the effect of anthropization over fitness-related traits might differ from our findings, obtained in a Mediterranean country, being mediated by different behavioral, ecological, and evolutionary mechanisms. As we explained in the Introduction, wolves in Italy had been subjected to higher and more prolonged anthropogenic pressures, than other populations in Europe. Like in the case of other mammals, this could have favored the selection of specific personality traits [100] that subsequently allowed individuals to thrive in human-dominated landscapes [101]. For example, in countries where wolves have always lived in areas with low human densities, individuals consistently seem to avoid human settlements and activities [102,103]. It is also unclear the extent to which hybridization with domestic dogs, which in Italy is high, could have made wolves more capable of tolerating human disturbance or more capable of processing carbohydrates and exploit food waste, as amylase activity is highly variable even among domestic dogs [104]. For example, although no evidence is available for gray wolves and domestic dogs, in North America hybrids between wolves and coyotes (Canis latrans), a human-tolerant canid, select more anthropized landscapes than wolves [105]. These findings, altogether with the fact that our recovered wolves did not constitute a randomly selected sample, but perhaps included individuals with some specifically shy personality traits, call for further research at the European scale. Apart from this, although our study focused on body mass, numerous studies has shown that a combination of different morphometric measures can better quantify sexual dimorphism [106]. Although these measures are usually collected from museum specimens, it is not impossible to obtain them from animals that are found dead. As our findings raise serious questions about the potential expansion of wolves in Europe, research groups should pool together their data through collaborative platforms, and improve their collection through harmonized protocols, to address them.

Supporting information

S1 Fig. Number of female and male wolves, between Abruzzo and Emilia-Romagna/Tuscany region.

The number of wolves is shown on the y-axis.


S2 Fig. Distribution of wolves of different age classes, between Abruzzo and Emilia-Romagna/Tuscany region.

The number of recovered wolves is shown on the y-axis.


S3 Fig. Distribution of recovered wolves, between Abruzzo and Emilia-Romagna/Tuscany region, across the different months of the year.


S4 Fig. Distribution of recovered wolves that had been involved in collisions with vehicles, between Abruzzo and Emilia-Romagna/Tuscany region.

The number of recovered wolves is shown on the y-axis.


S5 Fig. Directed Acyclic Graph (DAG), showing the causal relationship of interest and candidate confounders that were included in model selection (light color) and unobserved mediators (dark color).

Total body length is not shown, as the predictor was included in the model not as a confounder, but to rule out the part of body mass that did not depend upon body condition, but upon differences in the size of animals.


S6 Fig. Effect of the removal of redundant confounders, over the interaction between anthropization and the age in days of recovered wolves of 1 year of age.

Plots correspond to the first (a), second (b), third (c), and fourth (d) quartiles of the distribution of median Human Footprint Index, calculated in a buffer with a 6-km radius around the point where animals were found. Columns (a) to (d) therefore corresponds to increasingly urbanized areas, while rows to models selected in S1 Table.


S7 Fig. Effect of the removal of redundant confounders, over the interaction between anthropization and the weight dimorphism of adult wolves.

Plots correspond to the first (a), second (b), third (c) and fourth (quartiles) of the distribution of median Human Footprint Index, calculated in a buffer with 6-km radius around the point where animals were found. Columns (a) to (d) therefore corresponds to increasingly urbanized areas, while rows to models selected in S2 Table.


S8 Fig. Residuals versus fitted values from the best candidate model.


S9 Fig. Residuals from the best candidate model versus standardized and centered scores of the Human Footprint Index.


S10 Fig. Residuals from the best candidate model versus standardized and centered scores of the age in days of recovered wolves.


S11 Fig. Residuals from the best candidate model versus standardized and centered scores of the year when wolves were recovered.


S12 Fig. Residuals from the best candidate model versus standardized and centered scores of the total body length.


S13 Fig. Isotropic semivariogram: Semivariance of model observations in function of distance between sites where wolves were recovered.


S14 Fig. Residuals versus fitted values from the best candidate model.


S15 Fig. Residuals from the best candidate model versus standardized and centered scores of the Human Footprint Index.


S16 Fig. Residuals from the best candidate model versus standardized and centered scores of the total body length.


S17 Fig. Isotropic semivariogram: Semivariance of model observations in function of distance between sites where wolves were uncovered.


S1 Table. Theoretical expected log-pointwise predictive density (ELPD) and its standard error (S.E.), obtained from leave one-out cross-validation.

Outputs from models about the body mass of wolves in their first year of age (left).


S2 Table. Theoretical expected log-pointwise predictive density (ELPD) and its standard error (S.E.) from leave one-out cross-validation.

Outputs from models about adult wolves.



  1. 1. Di Minin E, Slotow R, Hunter LT, Montesino Pouzols F, Toivonen T, Verburg PH, et al. Global priorities for national carnivore conservation under land use change. Sci Rep 2016; 6:1–9. pmid:27034197
  2. 2. Chapron G, et al. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science. 2014; 346:1517–1519. pmid:25525247
  3. 3. Cimatti et al. Large carnivore expansion in Europe is associated with human population density and land cover changes. Divers Distrib. 2021; 27: 602–617.
  4. 4. Miller SD, McLellan BN, Derocher AE. Conservation and management of large carnivores in North America. Int J Environ Stud. 2014;70: 383–398.
  5. 5. Wolf C, Ripple WJ. Prey depletion as a threat to the world’s large carnivores. Roy Soc Open Sci. 2016; 3: 160252. pmid:27853599
  6. 6. Ditmer MA, Stoner DC, Francis CD, Barber JR, Forester JD, Choate DM, et al. Artificial nightlight alters the predator–prey dynamics of an apex carnivore. Ecography. 2021; 44: 149–161.
  7. 7. Wilmers CC, Nisi AC, Ranc N. COVID-19 suppression of human mobility releases mountain lions from a landscape of fear. Curr Biol. 2021; 31:3952–3955. pmid:34197728
  8. 8. Ripari L, et al. Human disturbance is the most limiting factor driving habitat selection of a large carnivore throughout Continental Europe. Biol Conserv. 2022; 266: 109446.
  9. 9. Kozakiewicz CP, Burridge CP, Funk WC, Salerno PE, Trumbo DR, Gagne RB, et al. Urbanization reduces genetic connectivity in bobcats (Lynx rufus) at both intra–and interpopulation spatial scales. Mol Ecol. 2019; 28: 5068–5085. pmid:31613411
  10. 10. Morales-González A, Ruiz-Villar H, Ordiz A, Penteriani V. Large carnivores living alongside humans: Brown bears in human-modified landscapes. Global Ecol Conserv. 2022; 22: e00937.
  11. 11. Millán J, López-Bao JV, García EJ, Oleaga Á, Llaneza L, Palacios V, et al. Patterns of exposure of Iberian wolves (Canis lupus) to canine viruses in human-dominated landscapes. Ecohealth. 2016; 13: 123–134.
  12. 12. Serieys LE, Lea AJ, Epeldegui M, Armenta TC, Moriarty J, VandeWoude S, et al. Urbanization and anticoagulant poisons promote immune dysfunction in bobcats. P Roy Soc B-Biol Sci. 2018; 285: 20172533. pmid:29343604
  13. 13. Carroll RP, Litvaitis MK, Foxall T. Bobcat Hair Cortisol Correlates with Land Use and Climate. J Wildlife Manage. 2021; 85: 772–781.
  14. 14. Lamb CT, Ford AT, McLellan BN, Proctor MF, Mowat G, Ciarniello L, et al. The ecology of human–carnivore coexistence. PNAS. 2020; 11: 17876–17883. pmid:32632004
  15. 15. Hantak MM, McLean BS, Li D, Guralnick RP. Mammalian body size is determined by interactions between climate, urbanization, and ecological traits. Commun Biol. 2021; 4: 1–10. pmid:34400755
  16. 16. Yom-Tov Y. Body sizes of carnivores commensal with humans have increased over the past 50 years. Funct Ecol. 2003; 323–327.
  17. 17. Lunn NJ, Stirling I. The significance of supplemental food to polar bears during the ice-free period of Hudson Bay. Can J Zool. 1985; 63: 2291–2297.
  18. 18. Green DS, Farr MT, Holekamp KE, Strauss ED, Zipkin EF. Can hyena behaviour provide information on population trends of sympatric carnivores?. Philos T Roy Soc B. 2019; 374: 20180052. pmid:31352879
  19. 19. Green DS, Johnson-Ulrich L, Couraud HE, Holekamp KE. Anthropogenic disturbance induces opposing population trends in spotted hyenas and African lions. Biodivers Conserv. 2018; 27: 871–889.
  20. 20. Abay GY, Bauer H, Gebrihiwot K, Deckers J. Peri-urban spotted hyena (Crocuta crocuta) in Northern Ethiopia: diet, economic impact, and abundance. Eur J Wildlife Res. 2011; 57: 759–765.
  21. 21. Newsome TM, Dellinger JA, Pavey CR, Ripple WJ, Shores CR, Wirsing AJ, et al. The ecological effects of providing resource subsidies to predators. Global Ecol Biogeogr. 2015; 24: 1–11.
  22. 22. Coon CA, Nichols BC, McDonald Z, Stoner DC. Effects of land-use change and prey abundance on the body condition of an obligate carnivore at the wildland-urban interface. Landscape Urban Plan. 2019; 192: 103648.
  23. 23. Bassi E, Willis SG, Passilongo D, Mattioli L, Apollonio M. Predicting the spatial distribution of wolf (Canis lupus) breeding areas in a mountainous region of Central Italy. PloS one. 2015; 10: e0124698. pmid:26035174
  24. 24. Apollonio M, Andersen R, Putman R. European ungulates and their management in the 21st century. Cambridge University Press, Cambridge; 2010.
  25. 25. Boitani L. (1992). Wolf research and conservation in Italy. Biol Conserv. 1992; 61: 125–132.
  26. 26. Vilén T, Gunia K, Verkerk PJ, Seidl R, Schelhaas MJ, Lindner M, et al. Reconstructed forest age structure in Europe 1950–2010. Forest Ecol Manag. 2012; 286: 203–218.
  27. 27. Hindrikson M, Remm J, Pilot M, Godinho R, Stronen AV, Baltrūnaité L, et al. Wolf population genetics in Europe: a systematic review, meta‐analysis and suggestions for conservation and management. Biol Rev. 2017; 92: 1601–1629. pmid:27682639
  28. 28. Jȩdrzejewski W, Jȩdrzejewska B, Okarma H, Schmidt K, Zub K, Musiani M. Prey selection and predation by wolves in Białowieża Primeval Forest, Poland. J Mammal. 2000; 81: 197.
  29. 29. Jędrzejewski W, Schmidt K, Theuerkauf J, Jędrzejewska B, Selva N, Zub K, et al. Kill rates and predation by wolves on ungulate populations in Białowieża Primeval Forest (Poland). Ecology. 2002; 83: 1341–1356.
  30. 30. Valdmann H, Koppa O, Looga A. Diet and prey selectivity of wolf Canis lupus in middle-and south-eastern Estonia. Balt For. 1998; 4: 42–46.
  31. 31. Olsson O, Wirtberg J, Andersson M, Wirtberg I. Wolf Canis lupus predation on moose Alces alces and roe deer Capreolus capreolus in south‐central Scandinavia. Wildlife Biol. 1997; 3: 13–25.
  32. 32. Gade-Jorgensen I, Stagegaard R. Diet composition of wolves Canis lupus in east-central Finland. Acta Theriol. 2000; 45: 537–547.
  33. 33. Van Der Veken T, Van Den Berge K, Gouwy J, Berlengee F, Schamp K. Diet of the first settled wolves (Canis lupus) in Flanders, Belgium. Lutra (Leiden). 2021; 64: 45–56.
  34. 34. Schley L, Jacobs M, Collet S, Kristiansen A, Herr J. First wolves in Luxembourg since 1893, originating from the Alpine and Central European populations. Mammalia. 2021; 85: 193–197.
  35. 35. Lelieveld G, Beekers B, Kamp J, Klees D, Linnartz L, van Norren E, et al. The first proof of the recent presence of wolves in the Netherlands. Lutra. 2016, 59: 23–31.
  36. 36. Sunde P, Collet S, Nowak C, Thomsen PF, Hansen MM, Schulz B, et al. Where have all the young wolves gone? Traffic and cryptic mortality create a wolf population sink in Denmark and northernmost Germany. Conserv Lett. 2021; 14: e12812.
  37. 37. Nakamura M, Rio-Maior H, Godinho R, Petrucci-Fonseca F, Álvares F. Source-sink dynamics promote wolf persistence in human-modified landscapes: Insights from long-term monitoring. Biol Conserv. 2021; 256, 109075.
  38. 38. Ražen N, Kuralt Ž, Fležar U, Bartol M, Černe R, Kos I., et al. Citizen science contribution to national wolf population monitoring: what have we learned?. Eur J Wildlife Res. 2020, 66: 1–9.
  39. 39. Louvrier J, Papaix J, Duchamp C, Gimenez O. A mechanistic–statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France. Spat Stat. 2020; 36: 100428.
  40. 40. Mech LD. Where can wolves live and how can we live with them?. Biol Conserv. 2017; 210: 310–317.
  41. 41. Zlatanova D, Ahmed A, Valasseva A, Genov P. Adaptive diet strategy of the wolf (Canis lupus L.) in Europe: a review. Acta zoologica bulgarica. 2014; 66: 439–452.
  42. 42. Newsome TM, Boitani L, Chapron G, Ciucci P, Dickman CR, Dellinger JA, et al. Food habits of the world’s grey wolves. Mammal Rev. 2016; 46: 255–269.
  43. 43. Lindström J. Early development and fitness in birds and mammals. Trends Ecol Evol. 1999, 14: 343–348. pmid:10441307
  44. 44. Imbert C, Caniglia R, Fabbri E, Milanesi P, Randi E, Serafini M, et al. Why do wolves eat livestock?: Factors influencing wolf diet in northern Italy. Biol Conserv. 2016; 195: 156–168.
  45. 45. Mech LD. Age-related body mass and reproductive measurements of gray wolves in Minnesota. J Mammal. 2006; 87: 80–84.
  46. 46. Stahler DR, MacNulty DR, Wayne RK, VonHoldt B, Smith DW. The adaptive value of morphological, behavioural and life‐history traits in reproductive female wolves. J Anim Ecol. 2013; 82: 222–234. pmid:23043440
  47. 47. Boitani L, Lovari S, Vigna Taglianti A. Fauna d’Italia. Mammalia III. Carnivora-Artiodactyla; 2003.
  48. 48. Morris JS, Brandt EK. Specialization for aggression in sexually dimorphic skeletal morphology in grey wolves (Canis lupus). J Anat. 2014; 225: 1–11. pmid:24810384
  49. 49. Mech LD, Wolf PC, Packard JM. Regurgitative food transfer among wild wolves. Can J Zool. 1999: 77; 1192–1195.
  50. 50. MacNulty DR, Smith DW, Mech LD, Eberly LE. Body size and predatory performance in wolves: is bigger better? J Anim Ecol. 2009; 78: 532–539. pmid:19175444
  51. 51. Hillis TL, Mallory FF. Sexual dimorphism in wolves (Canis lupus) of the Keewatin District, Northwest territories, Canada. Can J Zool. 1999; 74: 721–725.
  52. 52. Sand H, Wikenros C, Wabakken P, Liberg O. Effects of hunting group size, snow depth and age on the success of wolves hunting moose. Anim Behav. 2006; 72: 781–789.
  53. 53. Caniglia R, Fabbri E, Galaverni M, Milanesi P, Randi E. Noninvasive sampling and genetic variability, pack structure, and dynamics in an expanding wolf population. J Mammal. 2014; 95: 41–59.
  54. 54. Apollonio M, Bassi E, Berzi D, Bongi P, Caniglia R, Canu A, et al. Esperienze di monitoraggio e conservazione del lupo in Toscana (2013–2016). 2016.
  55. 55. Apollonio M. Monitoraggio del lupo in Toscana–Relazione anno 2023. 2023.
  56. 56. Ricci S, Fabrizio M, Artese C, Damiani G, Riganelli N, Locasciulli O, et al. Il monitoraggio della presenza del lupo nel Parco Nazionale del Gran Sasso e Monti della Laga. IX Congresso Italiano di Teriologia, Civitella Alfedena (AQ). 2014.
  57. 57. Musto C, Cerri J, Galaverni M, Caniglia R, Fabbri E, Mucci N, et al. Men and wolves: are anthropogenic causes the main driver of wolf mortality in human-dominated landscapes in Italy?. Global Ecol Conserv. 2021; 32: e01892.
  58. 58. Malandra F., Vitali A., Urbinati C., & Garbarino M. 70 years of land use/land cover changes in the Apennines (Italy): a meta-analysis. Forests. 2018; 9: 551.
  59. 59. Bassi E, Canu A, Firmo I, Mattioli L, Scandura M, Apollonio M. Trophic overlap between wolves and free-ranging wolf× dog hybrids in the Apennine Mountains, Italy. Global Ecol Conserv. 2017; 9: 39–49.
  60. 60. Bassi E, Gazzola A, Bongi P, Scandura M, Apollonio M. Relative impact of human harvest and wolf predation on two ungulate species in Central Italy. Ecol Res. 2020: 35; 662–674.
  61. 61. Capitani C, Bertelli I, Varuzza P, Scandura M, Apollonio M. A comparative analysis of wolf (Canis lupus) diet in three different Italian ecosystems. Mamm Biol. 2004; 69: 1–10.
  62. 62. Ciucci P, Mancinelli S, Boitani L, Gallo O, Grottoli L. Anthropogenic food subsidies hinder the ecological role of wolves: Insights for conservation of apex predators in human-modified landscapes. Global Ecol Conserv. 2020; 21: e00841.
  63. 63. Ferretti F, Lovari S, Mancino V, Burrini L, Rossa M. Food habits of wolves and selection of wild ungulates in a prey-rich Mediterranean coastal area. Mamm Biol. 2019; 99: 119–127.
  64. 64. Mattioli L, Apollonio M, Mazzarone V, Centofanti E. Wolf food habits and wild ungulate availability in the Foreste Casentinesi National Park, Italy. Acta theriol. 1995; 40: 387–402.
  65. 65. Mattioli L, Capitani C, Gazzola A, Scandura M, Apollonio M. Prey selection and dietary response by wolves in a high-density multi-species ungulate community. Eur J Wildl Res. 2011; 57: 909–922.
  66. 66. Mori E, Benatti L, Lovari S, Ferretti F. What does the wild boar mean to the wolf?. Eur J Wildl Res. 2017; 63: 1–5.
  67. 67. Banti P, Mazzarone V, Mattioli L, Ferretti M, Lenuzza A, Lopresti R, et al. A Step Change in Wild Boar Management in Tuscany Region, Central Italy. In: Kideghesho JR, editors. Managing Wildlife in a Changing World, 1st edn. IntechOpen, London. 2021.
  68. 68. Bräutigam K. R., Jörissen J., & Priefer C. The extent of food waste generation across EU-27: Different calculation methods and the reliability of their results. Waste Manage Rese. 2014; 32: 683–694. pmid:25161274
  69. 69. Mörner T, Eriksson H, Bröjer C, Nilsson K, Uhlhorn H, Ågren E, et al. Diseases and mortality in free-ranging brown bear (Ursus arctos), gray wolf (Canis lupus), and wolverine (Gulo gulo) in Sweden. J Wildlife Dis. 2005; 41: 298–303. pmid:16107663
  70. 70. Boitani L. Lupo Canis lupus. In: Pavan M (editors), Distribuzione e biologia di 22 specie di Mammiferi in Italia (pp. 61–67). Rome; 1981.
  71. 71. Randi E., Hulva P., Fabbri E., Galaverni M., Galov A., Kusak J., et al. Multilocus detection of wolf x dog hybridization and guidelines for marker selection. PLoS one. 2014; 9: e86409 pmid:24466077
  72. 72. Caniglia R., Galaverni M., Velli E., Mattucci F., Canu A., Apollonio M., et al. A standardized approach to empirically define reliable assignment thresholds and appropriate management categories in deeply introgressed populations. Sci Rep. 2020; 10: 2862. pmid:32071323
  73. 73. Labocha MK, Schutz H, Hayes JP. Which body condition index is best?. Oikos. 2014; 123: 111–119.
  74. 74. Lovari S, Sforzi A, Scala C, Fico R. Mortality parameters of the wolf in Italy: does the wolf keep himself from the door?. J Zool. 2007; 272: 117–124.
  75. 75. Hayes JP, Shonkwiler JS. Morphometric indicators of body condition: worthwhile or wishful thinking. In: Speakman JR, editors. Body composition analysis of animals. A handbook of non-destructive methods. Cambridge University Press, Cambridge. Pp 8–38.
  76. 76. Venter O, Sanderson EW, Magrach A, Allan JR, Beher J, Jones KR, et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci Data. 2016; 3: 1–10. pmid:27552448
  77. 77. Mancinelli S, Boitani L, Ciucci P. Determinants of home range size and space use patterns in a protected wolf (Canis lupus) population in the central Apennines, Italy. Can J Zool. 2018; 96: 828–838.
  78. 78. Mattioli L, Canu A, Passilongo D, Scandura M, Apollonio M. Estimation of pack density in grey wolf (Canis lupus) by applying spatially explicit capture-recapture models to camera trap data supported by genetic monitoring. Front Zool. 2018; 15: 1–15. pmid:30305834
  79. 79. Elwert F. Graphical causal models. In: Morgan SL, editors. Handbook of causal analysis for social research. Springer, New York; 2013. pp. 245–273.
  80. 80. Zanni M, Brogi R, Merli E, Apollonio M. The wolf and the city: how is this relationship evolving? Anim Conserv. 2022 (accepted, in press).
  81. 81. Umlauf N, Kneib T. A primer on Bayesian distributional regression. Stat Model. 2018; 18: 219–247.
  82. 82. Bürkner PC. brms: An R package for Bayesian multilevel models using Stan. J Stat Softw. 2017; 80: 1–28.
  83. 83. Lemoine NP. Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses. Oikos. 2019; 128: 912–928.
  84. 84. Vehtari A, Gelman A, Gabry J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat Comput. 2017; 27: 1413–1432.
  85. 85. Hilderbrand GV, Golden HN. Body composition of free-ranging wolves (Canis lupus). Can J Zool. 2013; 91: 1–6.
  86. 86. Petroelje TR, Belant JL, Beyer DE, Svoboda NJ. Subsidies from anthropogenic resources alter diet, activity, and ranging behavior of an apex predator (Canis lupus). Sci Rep. 2019; 9: 1–9. pmid:31530832
  87. 87. Axelsson E, Ratnakumar A, Arendt ML, Maqbool K, Webster MT, Perloski M, et al. The genomic signature of dog domestication reveals adaptation to a starch-rich diet. Nature. 2013; 495: 360–364. pmid:23354050
  88. 88. Lyu T, Liu G, Zhang H, Wang L, Zhou S, Dou H, et al. Changes in feeding habits promoted the differentiation of the composition and function of gut microbiotas between domestic dogs (Canis lupus familiaris) and gray wolves (Canis lupus). Amb Express. 2018; 8: 1–12. pmid:30073560
  89. 89. Isaksson C. Urbanization, oxidative stress and inflammation: a question of evolving, acclimatizing or coping with urban environmental stress. Funct Ecol. 2015; 29: 913–923.
  90. 90. Nickel BA, Suraci JP, Nisi AC, Wilmers CC. Energetics and fear of humans constrain the spatial ecology of pumas. PNAS. 2021; 118. pmid:33495339
  91. 91. Kruschke JK, Liddell TM. The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychon B Rev. 2018; 25: 178–206. pmid:28176294
  92. 92. Zimmermann B, Sand H, Wabakken P, Liberg O, Andreassen HP. Predator‐dependent functional response in wolves: From food limitation to surplus killing. J Anim Ecol. 2015; 84: 102–112. pmid:25109601
  93. 93. LeBlanc M, Festa-Bianchet M, Jorgenson JT. Sexual size dimorphism in bighorn sheep (Ovis canadensis): effects of population density. Can J Zool. 2001; 79: 1661–1670.
  94. 94. Ausband DE. Inherit the kingdom or storm the castle? Breeding strategies in a social carnivore. Ethology. 2022; 128: 152–158.
  95. 95. Ims RA, Hjermann DØ. Condition-dependent dispersal. In: Clobert J, Danchin E, Dhondt AA, Nichols JD, editors. Dispersal, 1st edn. Oxford University Press, Oxford. 1995. pp 203–216.
  96. 96. Bronson FH. Mammalian reproduction: an ecological perspective. Biology of reproduction. 1985; 32: 1–26. pmid:3882162
  97. 97. Manfredo MJ, Teel TL, Don Carlos AW, Sullivan L, Bright AD, Dietsch AM, et al. The changing sociocultural context of wildlife conservation. Conserv Biol. 2020; 34: 1549–1559. pmid:32128885
  98. 98. Apollonio M, Ciuti S, Pedrotti L, Banti P. Status and management of ungulates in Italy. In: Apollonio M, Andersen R, Putman R. European ungulates and their management in the 21st century. Cambridge University Press, Cambridge; 2010. pp. 475–506.
  99. 99. Morelle K, Bubnicki J, Churski M, Gryz J, Podgórski T, Kuijper DP. Disease-induced mortality outweighs hunting in causing wild boar population crash after african swine fever outbreak. Front Vet Sci. 2020; 378. pmid:32850993
  100. 100. Ciuti S, Muhly TB, Paton DG, McDevitt AD, Musiani M, Boyce MS. Human selection of elk behavioural traits in a landscape of fear. P Roy Soc B: Biol Sci. 2012; 279: 4407–4416. pmid:22951744
  101. 101. Griffin LL, Haigh A, Amin B, Faull J, Norman A, Ciuti S. Artificial selection in human‐wildlife feeding interactions. J Anim Ecol. 2022; 91: 1892–1905. pmid:35927829
  102. 102. Bojarska K, Maugeri L, Kuehn R, Król W, Theuerkauf J, Okarma H, et al. Wolves under cover: The importance of human-related factors in resting site selection in a commercial forest. Forest Ecol Manage. 2021; 497: 119511.
  103. 103. Sanchez DC, Zimmermann B, Wabakken P, Eriksen A, Milleret CP, Ordiz A, et al. Wolves at the door? Factors influencing the individual behavior of wolves in relation to anthropogenic features. Biol Conserv. 2020; 244:
  104. 104. Arendt M, Fall T, Lindblad‐Toh K, Axelsson E. Amylase activity is associated with AMY 2B copy numbers in dog: Implications for dog domestication, diet and diabetes. Animal genetics. 2014, 45, 716–722. pmid:24975239
  105. 105. Oliveira T, Benson JF, Thompson C, Patterson BR. Resource selection at homesites by wolves and eastern coyotes in a Canis hybrid zone. Ecosphere. 2022; 11: e03320.
  106. 106. MacLeod N, Kolska Horwitz L. Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania. BMC biology. 2020; 18: 1–26. pmid:32883273