Wolf predation on wild ungulates: how slope and habitat cover influence the localization of kill sites

Wolf predation on wild ungulates: how slope and habitat cover influence the localization of kill... Based on data collected along the Ligurian Apennines and Alps (N-W Italy), we analysed the main environmental and human-related factors influencing the distribution of kill sites of the wolf Canis lupus. We mapped and digitized 62 kill sites collected during 2007–2016. Around each kill site, we defined a buffer corresponding to the potential hunting area of wolves. We compared kill site plots and an equal number of random plots. We formulated a model of kill site distribu- tion following an approach presence versus availability by binary logistic regression analysis; we tested the hypothesis that wolf choice of kill sites is influenced by the physiography and the land use of the area. Among the preyed wild ungulates, we identified 23 roe deer Capreolus capreolus, 18 fallow deer Dama dama,16wildboars Sus scrofa,and 5chamois Rupicapra rupicapra.Binary logistic regression analysis showed a negative effect of the road density, the urban areas, the mixed forests, and a positive effect of steep slopes and open habitats. Prey are more vulnerable to predators under certain conditions and predators are capable of selecting for these conditions. Wolves achieved this by selecting particular habitats in which to kill their prey: they preferred steep, open habitats far from human presence, where wild ungulates are more easily detectable and chasable. Key words: Canis lupus, hunting habits, kill site distribution modeling, predator–prey interaction, wild ungulates. Among key factors of predator-prey interactions there is the use of function of prey distribution and predictability and environmental the space, as prey tend to minimize, while predators tend to maxi- factors that influence prey detection, access, or the success of an at- mize their spatial overlap (Sih 2005). Usually predators occupy terri- tack (McPhee et al. 2012). tories encompassing multiple prey species and adapt their spatial Environmental factors may affect both anti-predator and hunting and temporal behavior depending on the abundance, distribution, strategy of prey and predators. In particular, anti-predator and hunting and ecology of prey, in order to increase their encounter rate with strategy may vary depending on the type of habitat (Gervasi et al. them (Jenny and Zuberbu ¨ hler 2005; Harmsen et al. 2011; Torretta 2013), and the degree of habitat fragmentation (Zimmermann et al. et al. 2016). While hunting, predators need to not only identify 2014). The chance of a successful hunt for a predator depends on the space where they can obtain a higher probability of encounter with habitat type where it detects its prey and on the terrain through which prey, but also habitats that might increase their predation success. the pursuit takes place (Gorini et al. 2012). For example, certain types The idea introduced by McPhee et al. (2012) is that predation can of habitat may provide refugia from predation and others may affect be considered a “hierarchical process,” whereby predators are con- the degree of visibility influencing prey group size, vigilance, or activity strained to kill prey within the area they select while hunting. patterns (Gorini et al. 2012 and references therein). On the other hand, Therefore, kill sites are not randomly distributed (Hebblewhite et al. the cover present where prey is detected can affect the distance at which 2005; Kauffman et al. 2007); rather, where kill sites occur is a the chase can start (Husseman et al. 2003). The physical structure of V C The Author (2017). Published by Oxford University Press. 271 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Downloaded from https://academic.oup.com/cz/article-abstract/64/3/271/3804543 by Ed 'DeepDyve' Gillespie user on 21 June 2018 272 Current Zoology, 2018, Vol. 64, No. 3 habitats may allow prey to detect predators before these are within the sporadic presence along the boundaries with Piedmont and Emilia killing distance and thus allowing prey escape (Kunkel and Pletscher Romagna regions. 2001). Noninvasive genetic sampling estimated a minimum of 5 wolf During the past decades, humans have played, and still play, a packs between 2007 and 2013, with an average pack size of keystone role in shaping habitats characteristics. Most of the habitat 4.26 0.8 individuals (mean6 SD; Imbert et al. 2016). changes resulted from human activities: habitat loss and deterior- ation due to human exploitation, habitat fragmentation by transport Data collection and analysis and power lines, and habitat requalification have all an impact on We monitored wolf presence using the Tessellation Stratified ecosystems at various trophic levels (Vitousek et al. 1997, Ryall and Sampling (TSS) method (Barabesi and Franceschi 2011), which Fahrig 2006). Many researchers recognized the high variability in allows a better distribution of random samples and increases their the behavioral responses of carnivores to anthropogenic disturb- representativeness (Buckland et al. 2004; Barabesi and Fattorini ances (Hebblewhite and Merrill 2008) and their potentially import- 2013). Based on the estimated extent of an Apennines wolf pack ter- ant impacts on community structure and on predator–prey ritory (Ciucci et al. 1997; Caniglia et al. 2014), we subdivided the interactions (Hebblewhite et al. 2005). study region into 60 sample units of 10  10 km grid, as it corres- Wolves Canis lupus locally adapt their habits depending on those ponds to the average space requirements of a pack (Je ˛ drzejewski of their prey species, as they showed substantial spatial and tem- et al. 2008). In every sample unit, we randomly selected 1 transect poral overlap with species that constitute the bulk of their diet among the existing footpaths or secondary roads (total length (Torretta et al. 2016). At the same time, their movements and be- 288 km; min. ¼ 2.6 km; max. ¼ 10.4 km). From 2007 to 2014, we haviors are considerably affected by increasing intensity of human walked this transect net once a season (Spring: March–May; presence, which could give less time to search for the prey, to hunt, Summer: June–August; Autumn: September–November; Winter: to access, and consume an item (Musiani et al. 2010). Furthermore, December–February), so 4 times during each year, to collect wolf species’ habitat selection patterns at a fine scale are influenced by signs of presence (scats, prints, carcasses of preyed ungulates, urine), complex interactions between habitat attributes and human disturb- which were located with a GPS recorder and geo-referred using ances (Lesmerises et al. 2012). ArcGIS 9.3 (UTM coordinate system, WGS84-32N). Wolves are generalist apex predators, preying mainly on wild un- Collected samples corresponding to fresh scats, picking out the gulates (Peterson and Ciucci 2003, Mech et al. 2016). Packs roam external portions, urine, and hair were addressed to genetic analysis. within their exclusive territory and their members cooperate during The protocol used for the DNA extraction and analysis was fully ex- the hunt (Mech and Boitani 2003). Wolves are well adapted for cur- plained in Imbert et al. (2016). sorial predation with chases ranging from 100 m to >5km (Mech We delineated wolf range using the coordinates of genotypes be- and Boitani 2004). Many studies found that habitat characteristics longing to wolves. We used a fixed kernel estimator (Seaman and mediate predation by influencing the successful identification, the Powell 1996) and applied the reference smoothing factor (h ). We ref pursuit, and the capture of prey (Kunkel and Pletscher 2000, 2001; considered 95% isoplethes delineating wolf range (Laver and Kelly Kauffman et al. 2007; McPhee et al. 2012). The aim of this study is 2008). to identify the main environmental and human-related factors influ- We considered all the carcasses of wild ungulates preyed by encing the distribution of kill sites of the wolf in a Mediterranean re- wolves, both those detected during the monitoring and those re- gion, that is, the Ligurian Apennines and Alps (N-W Italy). ported and verified by trained people (e.g., wildlife researchers and volunteers involved in the monitoring) recorded during 2007–2016, reporting the preyed species and possibly some related information Materials and Methods (sex, age, proportion of consumption). We ascribed predation events Study area to wolf by observing the carcasses (shape and localization of The study was carried out in Liguria (5,343 km region in N-W wounds, consumption, and spacing of canine puncture wounds) and Italy; Figure 1); it is characterized by a broad altitude range, from the surroundings of the kill site (wolf signs of presence, e.g., scats, 0 to 2,200 m a.s.l. Climate is temperate continental with the eastern prints, and scratches). Around each kill site we defined a circular part of the region being more rainy and humid (inland average an- buffer corresponding to the potential hunting area of wolves. We nual precipitation: 2,000 mm) than the western part (1,000 mm). used a width of 13 km, corresponding to the average travel distance Forests cover 63.7% of the whole area (broad-leaved woods: of wolves during the night to go from dens or resting sites to hunting 28.7%; coniferous woods: 7.1%; mixed woods: 27.9%). Pastures sites in Italy (Ciucci et al. 1997). We compared the plots where kill and scrublands cover 5.3% and 10.3%, respectively. Cultivated sites were recorded and an equal number of random plots within the lands (12%) are localized along main valleys and permanent crops estimated wolf range. We formulated a model of kill site distribution are dominated by olive trees and vineyards. Urban areas (6.3%) are following an approach presence versus availability by binary logistic concentrated near the coasts and along flat and wide valleys. regression analysis (BLRA); we tested the hypothesis that wolf The wild ungulate community includes the wild boar Sus choice of kill sites is influenced by the physiography and the land scrofa, widely distributed with high densities (more than 20,000 use of the area. In each plot, we measured from the Corine Land individuals shot per year), the roe deer Capreolus capreolus, abun- Cover III level (scale 1:25,000) and the Digital Elevation Models dant in particular in the central part of the region (30.9 individuals (DEM; cell size 250 m) the environmental variables used to model per km ), the fallow deer Dama dama, present in the provinces of the kill site distribution: four slope classes, road and path density, Genoa and Savona (10.7 and 5.8 individuals per km , respect- forests, urban and cultivated areas, scrublands, open areas, and bare ively), and the chamois Rupicapra rupicapra, present only in the ground (Table 1). We ran the logistic regression (link function Alps (14.6 individuals per km ) (Wildlife Services of Ligurian “logit”) using the stepwise forward method; we set the Alpha-to- Provinces, unpublished data). Hunters annually harvest these un- Enter¼ 0.05 and the Alpha-to-Remove¼ 0.10. We considered poten- gulate species. Moreover, the red deer Cervus elaphus has a tial multicollinearity among variables using the variance inflation Downloaded from https://academic.oup.com/cz/article-abstract/64/3/271/3804543 by Ed 'DeepDyve' Gillespie user on 21 June 2018 Torretta et al.  Factors influencing wolf kill sites 273 Figure 1. On the top Liguria (in black) within Italy (dark gray); Liguria region with political boundaries (provinces) and wolf kill sites (dots) collected during 2007–2016. locations of wolf genotypes, pooled across years, we estimated a Table 1. Ecogeographical variables measured in the 13-km buffers wolf range with a total extent of 5,068 km . around the kill sites and used to model kill site distribution We mapped and digitized 62 kill sites by ArcGis 9.3; 57 cases Name Description Unit were collected from 2007 to 2014 during the wolf monitoring pro- ject and 5 were collected in 2015–2016 (Figure 1). Among the Slope 0 –19 % preyed wild ungulates, we identified 23 roe deer (37.1%), 18 fallow Slope 20 –39 % deer (29%), 16 wild boars (25.8%), and 5 chamois (8.1%). We Slope 40 –59 % found 1 prey in each kill site, with the exception of a multiple kill of Slope > 60 % fallow deer (n ¼ 3). Road density Paved roads km/km More than half of the carcasses were found during the snow Path density Paths and gravel roads km/km Urban areas Villages, industrial areas, % cover season (from January to March; n ¼ 38). 15 carcasses were transport units, urban parks found in the altitude range 400–800 m a.s.l.; 29 carcasses in the Broad-leaved forests % range 800–1,200 m; 12 carcasses in the range 1,200–1,600m; 6 car- Coniferous forests % casses in the range 1,600–2,000 m. Mixed forests % BLRA showed a negative effect of the mixed forests, the urban Open areas Pastures and natural grasslands % areas, and the road density, the latter without statistical significance, Cultivated lands Arable lands and permanent crops % a positive effect of steep slopes (> 60 ) and open areas. VIF values of Scrublands Shrub and herbaceous vegetation % these predictor variables (< 3) indicated the absence of serious mul- associations ticollinearity (Table 2). Bare grounds Rocks and areas with little or % The logistic model explained 45.6% of the variance of the re- no vegetation cover sponse variable and correctly classified 76.6% of original cases, 82.3% of kill sites, and 71% of control ones. The area under the ROC curve was significantly greater than that of a model that ran- factor (VIF); we retained VIF ¼ 3 as threshold value (Zuur et al. domly classifies the cases (AUC ¼ 0.8286 0.037; P< 0.001). 2010; Dormann et al. 2013). We tested the model performance by Finally, the P-value of the Hosmer–Lemeshow Goodness of Fit Test the percentage of correct classifications of original cases, was> 0.05 indicating a very good model fit (Table 2). Nagelkerke’s R , the receiver operating characteristic (ROC) curve analysis, and the Hosmer–Lemeshow Goodness of Fit Test (Hosmer and Lemeshow 2000). Discussion The environmental factors influencing the distribution of wolf kill Results sites detected in this study only partially correspond with those re- On average we surveyed 77.8% of transects per season. We identi- ported from previous studies. Wolf kill sites in Liguria were steep, fied 74 distinct wolf genotypes, corresponding to 189 non-invasive open habitats (e.g. pastures and grasslands) far from roads and DNA samples (98% feces, 1% urine, and 1% hair), collected in the urban areas. study area from 2007 to 2014; moreover, we identified 12 samples In agreement with Kunkel and Pletscher (2001), we found that belonging to wolves but without individual identification. Based on hiding-cover levels were lower at kill sites than at random sites. Downloaded from https://academic.oup.com/cz/article-abstract/64/3/271/3804543 by Ed 'DeepDyve' Gillespie user on 21 June 2018 274 Current Zoology, 2018, Vol. 64, No. 3 Table 2. Results of logistic regression analysis between kill sites plots (n ¼ 62) and random ones (n ¼ 62) Predictor variables B SE P e (odds ratio) VIF Slope > 60 0.749 0.309 0.015 2.116 1.901 Road density 0.766 0.425 0.071 0.465 1.941 Mixed forests 1.224 0.420 0.004 0.294 1.551 Open areas 0.685 0.259 0.008 1.984 1.066 Urban areas 1.210 0.604 0.045 0.298 2.287 Constant 0.444 0.297 0.135 0.641 Hosmer–Lemeshow test X ¼ 4.360 df ¼ 8 0.823 Indeed, dense cover can affect the prey capacity to exploit refuges, Muhly et al. (2011) suggested that high-human activity on roads thus enhance its chances of escaping an attack, and can increase the and trails displaced predators but not prey species, creating spatial chance of detection of the predator, because of its noisier approach refuge from predation during non-hunting seasons. Despite the fact (Balme et al. 2007). From the prey point of view, open habitats, as that encounters between wolves and ungulates might be more fre- pastures and grasslands, usually imply high visibility and high en- quent near linear features, as roads and trails (James and Stuart- counter rates with predators but also cause to be more easily alerted, Smith 2000; Kunkel and Pletscher 2000; Whittington et al. 2011), and thus affect the distance at which a hunter may start the chase at- the kill sites could be further away after the chase. tempt (Mills et al. 2004). From the predator point of view, in open In conclusion, although wolves select densely covered habitats to habitats, prey were easier to locate and catch. Wild ungulates mainly spend most of their time within their territory, they seem to select use open habitats during the night as feeding areas, because of the different habitats, while hunting, to finally kill prey. Forest cover higher quality resources, and more closed habitats during the day, was an important habitat variable influencing wolf distribution and with less forage but a higher degree of shelter (Bonnot et al. 2013 numbers (Je R drzejewski et al. 2004), the localization of dens and ren- and references therein). Hence, wolves have greater chances of en- dez-vous sites (Capitani et al. 2006), and ensuring abundance of countering wild ungulates while feeding in open areas. Wild ungu- prey species, that is, wild ungulates. Because wolves are socially lates have to face a constant trade-off between the choice of better organized in structured packs hunting within stable and exclusive food patches and predation risk. This trade-off is mediated by the territories, they know where to find wild prey and where these prey vigilance behavior, which requires exclusive visual attention to scan can be most vulnerable to their cursorial predation strategy the environment, thereby interrupting or slowing down foraging ac- (Bergman et al. 2006). Hebblewhite et al. (2005) found that topo- tivity (Lima 1995). Wolves usually take advantage of this wavering graphic features and habitat (i.e., vegetation) determined patterns of behavior to start the rush. Moreover, wolves are mainly active from wolf–prey encounters and mediated post-encounter outcomes. dawn to dusk and this is probably closely related to their hunting Pastures and natural grasslands lying along forest edges may corres- pattern, which matches with the activity patterns of wild ungulates pond to optimal habitats where to find abundant ungulates, particu- (Theuerkauf et al. 2003b, Torretta et al. 2016). If wolves chase larly during feeding time, and steep slopes guaranteed a vantage prey, open areas might be more suitable to finally kill the prey as position during the stalk, the encounter, the rush, and the chase of they can attack them simultaneously from several sides (Theuerkauf prey (Peterson and Ciucci 2003). At the same time, open habitats do and Rouys 2008). not provide refuges to prey and steep terrain may impose a slow es- An interesting, and not of secondary importance, finding of our cape, increasing the probability of capture. research was the effect of steep surfaces on kill site location. In our mountainous study area, terrain features appeared to be important in the wolf hunting strategy. In contrast to Kauffman et al. (2007), Acknowledgments which found that flat areas were the optimal hunting grounds for We thank A. Biondo, G. Cristiani, P. Genta, P. Pavesio, F. Puopolo, L. wolves, we found a positive effect of steep areas. In Liguria, flat Schenone, D. Signorelli, and F. Zucca for their field-work collaboration. areas usually occur at the valley bottom and close to the shoreline, Zhiyun Jia and two anonymous reviewers provided helpful comments to the where human presence is very high. Wolves may find a suitable draft of the manuscript. habitat by selecting steep slopes, in terms of advantage during hunt- ing activities: being on a vantage point possibly with few visual bar- riers, steep surfaces could enhance the wolf ability to sort through a prey group and scan its members to identify most vulnerable individ- Funding uals. In addition, Gula (2004) found that wolves killed most of their The collection of data was supported by the project “Il Lupo in Liguria” prey in creeks and deep ravines, where wild ungulates may be easier (2012–2014), funded by the Regional Administration of Liguria (ROP/ERDF to intercept, as they have to slow down and change gait. funds) and coordinated by the Antola Regional Park. 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Wolf predation on wild ungulates: how slope and habitat cover influence the localization of kill sites

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Abstract

Based on data collected along the Ligurian Apennines and Alps (N-W Italy), we analysed the main environmental and human-related factors influencing the distribution of kill sites of the wolf Canis lupus. We mapped and digitized 62 kill sites collected during 2007–2016. Around each kill site, we defined a buffer corresponding to the potential hunting area of wolves. We compared kill site plots and an equal number of random plots. We formulated a model of kill site distribu- tion following an approach presence versus availability by binary logistic regression analysis; we tested the hypothesis that wolf choice of kill sites is influenced by the physiography and the land use of the area. Among the preyed wild ungulates, we identified 23 roe deer Capreolus capreolus, 18 fallow deer Dama dama,16wildboars Sus scrofa,and 5chamois Rupicapra rupicapra.Binary logistic regression analysis showed a negative effect of the road density, the urban areas, the mixed forests, and a positive effect of steep slopes and open habitats. Prey are more vulnerable to predators under certain conditions and predators are capable of selecting for these conditions. Wolves achieved this by selecting particular habitats in which to kill their prey: they preferred steep, open habitats far from human presence, where wild ungulates are more easily detectable and chasable. Key words: Canis lupus, hunting habits, kill site distribution modeling, predator–prey interaction, wild ungulates. Among key factors of predator-prey interactions there is the use of function of prey distribution and predictability and environmental the space, as prey tend to minimize, while predators tend to maxi- factors that influence prey detection, access, or the success of an at- mize their spatial overlap (Sih 2005). Usually predators occupy terri- tack (McPhee et al. 2012). tories encompassing multiple prey species and adapt their spatial Environmental factors may affect both anti-predator and hunting and temporal behavior depending on the abundance, distribution, strategy of prey and predators. In particular, anti-predator and hunting and ecology of prey, in order to increase their encounter rate with strategy may vary depending on the type of habitat (Gervasi et al. them (Jenny and Zuberbu ¨ hler 2005; Harmsen et al. 2011; Torretta 2013), and the degree of habitat fragmentation (Zimmermann et al. et al. 2016). While hunting, predators need to not only identify 2014). The chance of a successful hunt for a predator depends on the space where they can obtain a higher probability of encounter with habitat type where it detects its prey and on the terrain through which prey, but also habitats that might increase their predation success. the pursuit takes place (Gorini et al. 2012). For example, certain types The idea introduced by McPhee et al. (2012) is that predation can of habitat may provide refugia from predation and others may affect be considered a “hierarchical process,” whereby predators are con- the degree of visibility influencing prey group size, vigilance, or activity strained to kill prey within the area they select while hunting. patterns (Gorini et al. 2012 and references therein). On the other hand, Therefore, kill sites are not randomly distributed (Hebblewhite et al. the cover present where prey is detected can affect the distance at which 2005; Kauffman et al. 2007); rather, where kill sites occur is a the chase can start (Husseman et al. 2003). The physical structure of V C The Author (2017). Published by Oxford University Press. 271 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Downloaded from https://academic.oup.com/cz/article-abstract/64/3/271/3804543 by Ed 'DeepDyve' Gillespie user on 21 June 2018 272 Current Zoology, 2018, Vol. 64, No. 3 habitats may allow prey to detect predators before these are within the sporadic presence along the boundaries with Piedmont and Emilia killing distance and thus allowing prey escape (Kunkel and Pletscher Romagna regions. 2001). Noninvasive genetic sampling estimated a minimum of 5 wolf During the past decades, humans have played, and still play, a packs between 2007 and 2013, with an average pack size of keystone role in shaping habitats characteristics. Most of the habitat 4.26 0.8 individuals (mean6 SD; Imbert et al. 2016). changes resulted from human activities: habitat loss and deterior- ation due to human exploitation, habitat fragmentation by transport Data collection and analysis and power lines, and habitat requalification have all an impact on We monitored wolf presence using the Tessellation Stratified ecosystems at various trophic levels (Vitousek et al. 1997, Ryall and Sampling (TSS) method (Barabesi and Franceschi 2011), which Fahrig 2006). Many researchers recognized the high variability in allows a better distribution of random samples and increases their the behavioral responses of carnivores to anthropogenic disturb- representativeness (Buckland et al. 2004; Barabesi and Fattorini ances (Hebblewhite and Merrill 2008) and their potentially import- 2013). Based on the estimated extent of an Apennines wolf pack ter- ant impacts on community structure and on predator–prey ritory (Ciucci et al. 1997; Caniglia et al. 2014), we subdivided the interactions (Hebblewhite et al. 2005). study region into 60 sample units of 10  10 km grid, as it corres- Wolves Canis lupus locally adapt their habits depending on those ponds to the average space requirements of a pack (Je ˛ drzejewski of their prey species, as they showed substantial spatial and tem- et al. 2008). In every sample unit, we randomly selected 1 transect poral overlap with species that constitute the bulk of their diet among the existing footpaths or secondary roads (total length (Torretta et al. 2016). At the same time, their movements and be- 288 km; min. ¼ 2.6 km; max. ¼ 10.4 km). From 2007 to 2014, we haviors are considerably affected by increasing intensity of human walked this transect net once a season (Spring: March–May; presence, which could give less time to search for the prey, to hunt, Summer: June–August; Autumn: September–November; Winter: to access, and consume an item (Musiani et al. 2010). Furthermore, December–February), so 4 times during each year, to collect wolf species’ habitat selection patterns at a fine scale are influenced by signs of presence (scats, prints, carcasses of preyed ungulates, urine), complex interactions between habitat attributes and human disturb- which were located with a GPS recorder and geo-referred using ances (Lesmerises et al. 2012). ArcGIS 9.3 (UTM coordinate system, WGS84-32N). Wolves are generalist apex predators, preying mainly on wild un- Collected samples corresponding to fresh scats, picking out the gulates (Peterson and Ciucci 2003, Mech et al. 2016). Packs roam external portions, urine, and hair were addressed to genetic analysis. within their exclusive territory and their members cooperate during The protocol used for the DNA extraction and analysis was fully ex- the hunt (Mech and Boitani 2003). Wolves are well adapted for cur- plained in Imbert et al. (2016). sorial predation with chases ranging from 100 m to >5km (Mech We delineated wolf range using the coordinates of genotypes be- and Boitani 2004). Many studies found that habitat characteristics longing to wolves. We used a fixed kernel estimator (Seaman and mediate predation by influencing the successful identification, the Powell 1996) and applied the reference smoothing factor (h ). We ref pursuit, and the capture of prey (Kunkel and Pletscher 2000, 2001; considered 95% isoplethes delineating wolf range (Laver and Kelly Kauffman et al. 2007; McPhee et al. 2012). The aim of this study is 2008). to identify the main environmental and human-related factors influ- We considered all the carcasses of wild ungulates preyed by encing the distribution of kill sites of the wolf in a Mediterranean re- wolves, both those detected during the monitoring and those re- gion, that is, the Ligurian Apennines and Alps (N-W Italy). ported and verified by trained people (e.g., wildlife researchers and volunteers involved in the monitoring) recorded during 2007–2016, reporting the preyed species and possibly some related information Materials and Methods (sex, age, proportion of consumption). We ascribed predation events Study area to wolf by observing the carcasses (shape and localization of The study was carried out in Liguria (5,343 km region in N-W wounds, consumption, and spacing of canine puncture wounds) and Italy; Figure 1); it is characterized by a broad altitude range, from the surroundings of the kill site (wolf signs of presence, e.g., scats, 0 to 2,200 m a.s.l. Climate is temperate continental with the eastern prints, and scratches). Around each kill site we defined a circular part of the region being more rainy and humid (inland average an- buffer corresponding to the potential hunting area of wolves. We nual precipitation: 2,000 mm) than the western part (1,000 mm). used a width of 13 km, corresponding to the average travel distance Forests cover 63.7% of the whole area (broad-leaved woods: of wolves during the night to go from dens or resting sites to hunting 28.7%; coniferous woods: 7.1%; mixed woods: 27.9%). Pastures sites in Italy (Ciucci et al. 1997). We compared the plots where kill and scrublands cover 5.3% and 10.3%, respectively. Cultivated sites were recorded and an equal number of random plots within the lands (12%) are localized along main valleys and permanent crops estimated wolf range. We formulated a model of kill site distribution are dominated by olive trees and vineyards. Urban areas (6.3%) are following an approach presence versus availability by binary logistic concentrated near the coasts and along flat and wide valleys. regression analysis (BLRA); we tested the hypothesis that wolf The wild ungulate community includes the wild boar Sus choice of kill sites is influenced by the physiography and the land scrofa, widely distributed with high densities (more than 20,000 use of the area. In each plot, we measured from the Corine Land individuals shot per year), the roe deer Capreolus capreolus, abun- Cover III level (scale 1:25,000) and the Digital Elevation Models dant in particular in the central part of the region (30.9 individuals (DEM; cell size 250 m) the environmental variables used to model per km ), the fallow deer Dama dama, present in the provinces of the kill site distribution: four slope classes, road and path density, Genoa and Savona (10.7 and 5.8 individuals per km , respect- forests, urban and cultivated areas, scrublands, open areas, and bare ively), and the chamois Rupicapra rupicapra, present only in the ground (Table 1). We ran the logistic regression (link function Alps (14.6 individuals per km ) (Wildlife Services of Ligurian “logit”) using the stepwise forward method; we set the Alpha-to- Provinces, unpublished data). Hunters annually harvest these un- Enter¼ 0.05 and the Alpha-to-Remove¼ 0.10. We considered poten- gulate species. Moreover, the red deer Cervus elaphus has a tial multicollinearity among variables using the variance inflation Downloaded from https://academic.oup.com/cz/article-abstract/64/3/271/3804543 by Ed 'DeepDyve' Gillespie user on 21 June 2018 Torretta et al.  Factors influencing wolf kill sites 273 Figure 1. On the top Liguria (in black) within Italy (dark gray); Liguria region with political boundaries (provinces) and wolf kill sites (dots) collected during 2007–2016. locations of wolf genotypes, pooled across years, we estimated a Table 1. Ecogeographical variables measured in the 13-km buffers wolf range with a total extent of 5,068 km . around the kill sites and used to model kill site distribution We mapped and digitized 62 kill sites by ArcGis 9.3; 57 cases Name Description Unit were collected from 2007 to 2014 during the wolf monitoring pro- ject and 5 were collected in 2015–2016 (Figure 1). Among the Slope 0 –19 % preyed wild ungulates, we identified 23 roe deer (37.1%), 18 fallow Slope 20 –39 % deer (29%), 16 wild boars (25.8%), and 5 chamois (8.1%). We Slope 40 –59 % found 1 prey in each kill site, with the exception of a multiple kill of Slope > 60 % fallow deer (n ¼ 3). Road density Paved roads km/km More than half of the carcasses were found during the snow Path density Paths and gravel roads km/km Urban areas Villages, industrial areas, % cover season (from January to March; n ¼ 38). 15 carcasses were transport units, urban parks found in the altitude range 400–800 m a.s.l.; 29 carcasses in the Broad-leaved forests % range 800–1,200 m; 12 carcasses in the range 1,200–1,600m; 6 car- Coniferous forests % casses in the range 1,600–2,000 m. Mixed forests % BLRA showed a negative effect of the mixed forests, the urban Open areas Pastures and natural grasslands % areas, and the road density, the latter without statistical significance, Cultivated lands Arable lands and permanent crops % a positive effect of steep slopes (> 60 ) and open areas. VIF values of Scrublands Shrub and herbaceous vegetation % these predictor variables (< 3) indicated the absence of serious mul- associations ticollinearity (Table 2). Bare grounds Rocks and areas with little or % The logistic model explained 45.6% of the variance of the re- no vegetation cover sponse variable and correctly classified 76.6% of original cases, 82.3% of kill sites, and 71% of control ones. The area under the ROC curve was significantly greater than that of a model that ran- factor (VIF); we retained VIF ¼ 3 as threshold value (Zuur et al. domly classifies the cases (AUC ¼ 0.8286 0.037; P< 0.001). 2010; Dormann et al. 2013). We tested the model performance by Finally, the P-value of the Hosmer–Lemeshow Goodness of Fit Test the percentage of correct classifications of original cases, was> 0.05 indicating a very good model fit (Table 2). Nagelkerke’s R , the receiver operating characteristic (ROC) curve analysis, and the Hosmer–Lemeshow Goodness of Fit Test (Hosmer and Lemeshow 2000). Discussion The environmental factors influencing the distribution of wolf kill Results sites detected in this study only partially correspond with those re- On average we surveyed 77.8% of transects per season. We identi- ported from previous studies. Wolf kill sites in Liguria were steep, fied 74 distinct wolf genotypes, corresponding to 189 non-invasive open habitats (e.g. pastures and grasslands) far from roads and DNA samples (98% feces, 1% urine, and 1% hair), collected in the urban areas. study area from 2007 to 2014; moreover, we identified 12 samples In agreement with Kunkel and Pletscher (2001), we found that belonging to wolves but without individual identification. Based on hiding-cover levels were lower at kill sites than at random sites. Downloaded from https://academic.oup.com/cz/article-abstract/64/3/271/3804543 by Ed 'DeepDyve' Gillespie user on 21 June 2018 274 Current Zoology, 2018, Vol. 64, No. 3 Table 2. Results of logistic regression analysis between kill sites plots (n ¼ 62) and random ones (n ¼ 62) Predictor variables B SE P e (odds ratio) VIF Slope > 60 0.749 0.309 0.015 2.116 1.901 Road density 0.766 0.425 0.071 0.465 1.941 Mixed forests 1.224 0.420 0.004 0.294 1.551 Open areas 0.685 0.259 0.008 1.984 1.066 Urban areas 1.210 0.604 0.045 0.298 2.287 Constant 0.444 0.297 0.135 0.641 Hosmer–Lemeshow test X ¼ 4.360 df ¼ 8 0.823 Indeed, dense cover can affect the prey capacity to exploit refuges, Muhly et al. (2011) suggested that high-human activity on roads thus enhance its chances of escaping an attack, and can increase the and trails displaced predators but not prey species, creating spatial chance of detection of the predator, because of its noisier approach refuge from predation during non-hunting seasons. Despite the fact (Balme et al. 2007). From the prey point of view, open habitats, as that encounters between wolves and ungulates might be more fre- pastures and grasslands, usually imply high visibility and high en- quent near linear features, as roads and trails (James and Stuart- counter rates with predators but also cause to be more easily alerted, Smith 2000; Kunkel and Pletscher 2000; Whittington et al. 2011), and thus affect the distance at which a hunter may start the chase at- the kill sites could be further away after the chase. tempt (Mills et al. 2004). From the predator point of view, in open In conclusion, although wolves select densely covered habitats to habitats, prey were easier to locate and catch. Wild ungulates mainly spend most of their time within their territory, they seem to select use open habitats during the night as feeding areas, because of the different habitats, while hunting, to finally kill prey. Forest cover higher quality resources, and more closed habitats during the day, was an important habitat variable influencing wolf distribution and with less forage but a higher degree of shelter (Bonnot et al. 2013 numbers (Je R drzejewski et al. 2004), the localization of dens and ren- and references therein). Hence, wolves have greater chances of en- dez-vous sites (Capitani et al. 2006), and ensuring abundance of countering wild ungulates while feeding in open areas. Wild ungu- prey species, that is, wild ungulates. Because wolves are socially lates have to face a constant trade-off between the choice of better organized in structured packs hunting within stable and exclusive food patches and predation risk. This trade-off is mediated by the territories, they know where to find wild prey and where these prey vigilance behavior, which requires exclusive visual attention to scan can be most vulnerable to their cursorial predation strategy the environment, thereby interrupting or slowing down foraging ac- (Bergman et al. 2006). Hebblewhite et al. (2005) found that topo- tivity (Lima 1995). Wolves usually take advantage of this wavering graphic features and habitat (i.e., vegetation) determined patterns of behavior to start the rush. Moreover, wolves are mainly active from wolf–prey encounters and mediated post-encounter outcomes. dawn to dusk and this is probably closely related to their hunting Pastures and natural grasslands lying along forest edges may corres- pattern, which matches with the activity patterns of wild ungulates pond to optimal habitats where to find abundant ungulates, particu- (Theuerkauf et al. 2003b, Torretta et al. 2016). If wolves chase larly during feeding time, and steep slopes guaranteed a vantage prey, open areas might be more suitable to finally kill the prey as position during the stalk, the encounter, the rush, and the chase of they can attack them simultaneously from several sides (Theuerkauf prey (Peterson and Ciucci 2003). At the same time, open habitats do and Rouys 2008). not provide refuges to prey and steep terrain may impose a slow es- An interesting, and not of secondary importance, finding of our cape, increasing the probability of capture. research was the effect of steep surfaces on kill site location. In our mountainous study area, terrain features appeared to be important in the wolf hunting strategy. In contrast to Kauffman et al. (2007), Acknowledgments which found that flat areas were the optimal hunting grounds for We thank A. Biondo, G. Cristiani, P. Genta, P. Pavesio, F. Puopolo, L. wolves, we found a positive effect of steep areas. In Liguria, flat Schenone, D. Signorelli, and F. Zucca for their field-work collaboration. areas usually occur at the valley bottom and close to the shoreline, Zhiyun Jia and two anonymous reviewers provided helpful comments to the where human presence is very high. Wolves may find a suitable draft of the manuscript. habitat by selecting steep slopes, in terms of advantage during hunt- ing activities: being on a vantage point possibly with few visual bar- riers, steep surfaces could enhance the wolf ability to sort through a prey group and scan its members to identify most vulnerable individ- Funding uals. In addition, Gula (2004) found that wolves killed most of their The collection of data was supported by the project “Il Lupo in Liguria” prey in creeks and deep ravines, where wild ungulates may be easier (2012–2014), funded by the Regional Administration of Liguria (ROP/ERDF to intercept, as they have to slow down and change gait. funds) and coordinated by the Antola Regional Park. 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