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Lion Hunting Behaviour and Vegetation Structure in an African Savanna

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Lion Hunting Behaviour and Vegetation Structure in an African Savanna Lion hunting behaviour and vegetation structure in an African savanna a,* b,c,1 a,2 Scott R. Loarie , Craig J. Tambling , Gregory P. Asner a Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, U.S.A. b Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, Pretoria, South Africa c Centre for African Conservation Ecology, Department of Zoology, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa Emerging evidence suggests that male lions are not dependent on female’s hunting skills but are in fact successful hunters. But difficulty locating kills and objectively characterizing landscapes has complicated the comparison of male and female lion hunting strategies. We used airborne Light Detection and Ranging (LiDAR) measurements of vegetation structure in Kruger National Park, combined with global positioning system (GPS) telemetry data on lion, Panthera leo, kills to quantify lines-of-sight where lion kills occurred compared with areas where lions rested, while controlling for time of day. We found significant differences in use of vegetation structure by male and female lions during hunts. While male lions killed in landscapes with much shorter lines- of-sight (16.2 m) than those in which they rested, there were no significant differences for female lions. These results were consistent across sizes of prey species. The influence of vegetation structure in shaping predatoreprey interactions is often hypothe-sized, but quantitative evidence has been scarce. Although our sample sizes were limited, our results provide a mechanism, ambush hunting versus social hunting in the open, to explain why hunting success of male lions might equal that of females. This study serves as a case study for more complete studies with larger samples sizes and illustrates how LiDAR and GPS telemetry can be used to provide new insight into lion hunting behaviour. Keywords: Bayesian statistics Carnegie Airborne Observatory GPS telemetry LiDAR lion Panthera leo predatoreprey interaction vegetation structure With large mammals increasingly confined to protected areas Sustaining predatoreprey interactions within these protected (Ceballos et al. 2005; Karanth et al. 2010), understanding how to areas will require a better understanding of how spatial heteroge- maintain landscape-scale ecological processes that support diverse neity influences predation strategies. Several recent studies have mammal communities within reserve boundaries is a critical con- begun to explore the role of spatial heterogeneity in shaping the servation priority (Woodroffe & Ginsberg 1998; Bengtsson et al. distribution of predatoreprey interactions across landscapes 2003; Berger 2004; van Aarde & Jackson 2007; Loarie et al. 2009a, (Hopcraft et al. 2005; Valeix et al. 2009). For example, the decline of b). Sustainable predatoreprey interactions exemplify con-ditions roan, Hippotragus equinus, and other antelope that tolerate sparse that are difficult to achieve within protected areas (Sinclair et al. surface water to avoid high predator densities has been partially 2003, 2008; Tambling & Du Toit 2005; Ripple & Beschta 2006; attributed to lions following water-dependent antelope into arid Hayward & Somers 2009). Pressure to offer charismatic viewing landscapes after the construction of waterholes (Grant et al. 2007; opportunities to tourists (Hutton & Leader-Williams 2003; Lindsey Hayward et al. 2007). How spatial heterogeneity in vegetation et al. 2007) has led many small reserves to reintroduce lions, Pan- structure influences predatoreprey interactions is less well un- thera leo, as exemplified by Slotow & Hunter’s (2009) study of lion derstood. Several studies suggest that predator ambush opportu- introductions into 37 small reserves across South Africa. Despite nities provided by vegetation structure could shape the being indigenous to most of these reserves, lions are returning to distribution and foraging behaviour of herbivores across a very different landscapes, namely those that are increasingly being landscape, but quantitative data are scarce (Hopcraft et al. 2005; fenced and fragmented with altered hydrological and fire regimes Fischhoff et al. 2007; Valeix et al. 2009). Such interactions between (Loarie et al. 2009a). vegetation structure and predation would be significant because vegetation structure is readily manipulated by park managers through fire, elephant, Loxodonta africana, exclusion and other * Correspondence: S. R. Loarie, Department of Global Ecology, Carnegie Institu- controls (Asner et al. 2009; Levick et al. 2009). Understanding tion for Science, 260 Panama Street, Stanford, CA 94305, U.S.A. possible feedbacks between vegetation structure and predation E-mail address: [email protected] (S. R. Loarie). 1 E-mail address: [email protected] (C. J. Tambling). success may elucidate both unintended consequences of and 2 E-mail address: [email protected] (G. P. Asner). opportunities arising from management activities. 1 Testing for the influence of vegetation structure on predatore the remotely accessed movement data from the GPS collars, clus- prey interactions has been complicated by two factors. First, most ters of GPS positions where lions were stationary for more than 2 h studies locate predation sites by following predators that are were identified as potential resting or kill sites. GPS clusters were difficult to observe in dense vegetation (Funston et al. 2001), investigated on foot to determine the activity at each GPS cluster especially at night when most hunting occurs (Mills & Biggs 1993), (Tambling et al. 2010). and thus, are potentially biased by sampling along roads and in the We identified kills by the presence of stomach contents that presence of observers. We apply global position sys-tem (GPS) were usually accompanied by carcass remains (bones, hair, horns telemetry for an unbiased sampling of predator loca-tions across or teeth). The stomach contents were chosen as diagnostic of the heterogeneous landscapes. Second, vegetation structure influences kill site because, while lions may move the carcass from the kill site ambush opportunities through the local viewshed (i.e. the (Schaller 1972), at Kruger, the stomach contents are almost always distance travelled by lines-of-sight from a location before they deposited within a few metres of the kill site (P. Funston, personal become obstructed). Because viewsheds are difficult to communication). This explains why stomach contents were occa- quantitatively measure in the field, ad hoc data on shrub and grass sionally separated from other carcass remains. Although we cover are used as proxies for lines-of-sight. Alternatively, assumed that the stomach content location was the site where the vegetation has been ignored, with viewsheds characterized only kill was made, we accommodated potential movement of the car- from coarse topographic features such as rock kopjes and canyons cass (before the stomach contents were emptied) in our analysis by (Hopcraft et al. 2005). We used LiDAR data to model how propagating a 10 m radius around the location. vegetation structure and other fine-scale landscape structures If no stomach contents or other carcass remains were found, we shape viewsheds consistently and objectively across the study area. assumed the lions were resting in unfavourable conditions to ini- tiate a hunt or perhaps recovering from a failed hunting attempt. To Lions are of particular interest because of their strong sexual minimize the incidence of failing to detect carcass remains, and dimorphism in both appearance and behaviour. Although male thereby misidentify kill locations as resting locations, we restricted lions were generally thought to be less accomplished hunters than our analysis to clusters checked within 16 weeks of their occur- females (Scheel & Packer 1991; Stander 1992), recent research rence. After this period of time, success in detecting kills decreases suggests that males successfully kill as frequently as females (Tambling et al. 2010). (Funston et al. 2001). Funston et al. (2001) found that prey species largely explained differences between male and female hunting LiDAR Data behaviour. These authors found no consistent influence of vegeta- tion structure, probably because of the difficulties of monitoring We mapped 100 km2 in the Satara region of KNP with the Car- lion hunting behaviour, as mentioned above. negie Airborne Observatory (CAO) (Asner et al. 2007), an integrated Here, we combined fine-scale airborne Light Detection and LiDAR and hyperspectral system, in April/May 2008. The CAO LiDAR Ranging (LiDAR) measurements with locations of lion predation subsystem provides three-dimensional structural information of events from Kruger National Park (KNP) in South Africa. From these vegetation canopies and the underlying terrain surface. The GPS-IMU data, we formulated a probabilistic model to quantify how vege- subsystem provides three-dimensional position and orientation data tation structure shapes the viewsheds used by the lions. Impor- for the CAO sensors, allowing for highly precise and accurate pro- tantly, our Bayesian framework allowed us to propagate jection of LiDAR observations on the ground. For this study, the CAO uncertainty from lines-of-sight to viewsheds and to consider the datawere collected from 2000 m above ground level, providing maps impact of sample size. We then tested for the influence of lion sex, of ground elevation, woody canopy height and three-dimensional lion hunting or
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