Complementing GPS Cluster Analysis with Activity Data for Studies of Leopard Ipanthera Pardus) Diet

Complementing GPS cluster analysis with activity data for studies of leopard IPanthera pardus) diet

Marien Fröhlich^ ", Anne Berger\ Stephanie Kramer-Schadt', lija Heckmann^ & Quinton iVIartins^" ^Leibniz Institute for Zoo and Wiidiife Research, Aifred-Kowali^e-Strasse 17, D-10315 Beriin, Germany ^Max Planok Institute for Ornithology, Eberhard-Gwinner-Strasse, D-82319 Seewiesen, Germany ^The Cape Leopard Trust, RO. Box 1118, Sun Valley Cape Town, 7985 Soutti Africa "School of Bioiogical Sciences, University of Bristol, Woodland Road, Bristol BS8 1UG, U.K.

Received 21 December 2011, Accepted 24 August 2012 Despite their wide distribution, feeding habits of leopards, Panthera pardus, outside savanna and forest habitats are poorly understood. We explored a novel approach of combining both GPS cluster and activity data analysis to study the hunting activity of a single female leopard in the Cederberg Mountains of the Western Cape, South Africa. Positions and acceleration data were obtained using a Vectronic GPS-PLUS collar. In total, 1760 GPS positions with a fix success of 87% were obtained between June 2008 and February 2009. Fifty-four of 78 potential kill sites identified from GPS data records were investigated 171 ±91 days (mean ± S.D.) after the potential prédation event which resulted in the detection of prey remains at 31 sites (success rate of 57.4%). Activity pattern was different at small-kill (rock hyrax; Hewitt's rock rabbit, Pronolagus saundersiae) sites compared to large-kill (antelope) sites, although data did not achieve significance {P= 0.07). Results of frequency analyses of activity data allowed the differentiation between feeding and non-feeding activity. The combination of different methods such as GPS telemetry and activity measurement provides a valuable means for detecting kill sites in rugged and largely inaccessible regions where direct observations and scat collection are difficult. Key words: Cape leopard, Panthera pardus, GPS cluster analysis, feeding ecology, prédation, activity, biological rhythm.

INTRODUCTION like cougars {Puma concolor, Anderson & Lindzey The feeding ecology and activity patterns of 2003; Knopff efa/. 2010) and wolves (Can/s/upus; leopards, Panthera pardus, have been studied Sand et al. 2005; Demma et al. 2007). Recent intensively in savanna and forest habitats {e.g. studies in sub-Saharan Africa, focusing on lions Bailey 1993; Jenny & Zuberbühier 2005), but little {Panthera /eo;Tambling etal. 2010) and leopards information is available from rugged mountain (Martins efa/. 2011) showed that these carnivores areas such as the Cederberg Mountains of the remain at kill sites for extended periods up to >24 Western Cape, South Africa (Norton et al. 1986; hours. However, distinguishing non-kill sites from Martins efa/. 2011). Most leopard diet studies in sites where small prey was killed remains an southern Africa have used faecal analysis or direct acknowledged challenge (Merrill ef al. 2010). observations (Bothma & Le Riche 1984; Norton Although GPS cluster analysis as well as the use efa/. 1986; Le Roux & Skinner 1989; Bailey 1993). of acceleration data from GPS collars are well However, direct observation and scat location of established in behavioural and ecological studies large carnivores in remote and mountainous {e.g. Anderson & Lindzey 2003; Gervasi ef al. regions are difficult, making studies of their feed- 2006; Löttker etal. 2009; Merrill etal. 2010), both ing habits challenging (Knopff ef al. 2009; Martins methods have never been combined to gain more ef ai. 2011). In the past decade, the use of detailed information on feeding behaviour of large GPS location data has provided valuable insight predators despite the fact that both kinds of data into the diet and prey selection of elusive predators are recorded simultaneously in modern collars *To whom correspondence should be addressed. (^PS collars from Lotek Engineering; e.g. E-mail: [email protected] Coulombe efa/. 2006; GPS Collars from Vectronic South African Journal of Wildlife Research 42(2): 104-110 (October 2012) Fröhlich etai.: GPS cluster analysis and activity data in studies of leopard diet 105

Table 1. Prey items found at kill sites of female leopard Fl 0. Mean weights derived from Skinner & Chimimba (2005).

Prey species Scientific name Mean hours spent Mean weight (kg) % of total at kill site (range) biomass

Rock hyrax Procavia capensis 17 7.7 (4-55) 3.66 29.3 Klipspringer Oreotragus oreotragus 7 29(10-48) 11.90 39.7 Common duiker Syivicapra grimmia 2 28 (24-32) 16.10 17.9 Cape grysbok Raphicerus melanotis 1 6 10.25 4.5 Cape porcupine i-iystrix africaeaustraiis 1 17 12.15 7.9 Hewitt's red rock rabbit Pronolagus saundersiae 1 4 1.62 0.7

Aerospace, e.g. Löttker ef ai. 2009). GPS duster anaiysis Within the scope of the Cape Leopard Trust Location data were plotted in ArcGIS 9.2 Cederberg project, the study explored the possibil- (ESRI 2010) and sequentially inspected to identify ities of a combined use of both GPS and activity location clusters. GPS location clusters that could data in order to gain further insight into the behav- signify potential feeding sites were defined as >2 ioural ecology and feeding habits of leopards in locations within 100 m of each other over a minimum the Cederberg Mountains. Our aims were (i) to 4-hour period based on the used GPS schedule describe leopard feeding habits by analysing (Knopff efa/. 2009,2010). Because GPS locations GPS cluster locations, and (ii) to determine feed- are somewhat inaccurate (Webb etai. 2008), and ing patterns using activity data correlated with the because kill remains may be scattered around GPS cluster analysis used to find kills. actual positions, a radius of 100 m within the selected positions was searched thoroughly for METHODS prey remains. Bones, hair, horns, feet and hooves were collected and used to identify prey species. If Study area no prey remains were found after intensive search, The rugged Cederberg Mountains, Western a cluster was termed as non-kill cluster (Martins Cape, South Africa, (32°27'S; 19°25'E), are situ- efa/. 2011). ated about 200 km north of Cape Town encom- passing approximately 2000 km^ of both Fynbos Statisticai anaiyses and Succulent Karoo biomes (Martins 2010). Activity data were broadly scattered and therefore averaged for the respective hours before and Data coiiection after cluster onset. Large kills (prey size >5 kg) The adult female leopard F10 was trapped by the were distinguished from small kills (prey Cape Leopard Trust research team on 18 June size <5 kg, see Table 1). Generalized additive 2008 and collared with a GPS radio-collar models (binomial GAM; package mgcv, Wood (Vectronic Aerospace GmbH, Berlin, Germany), 2006) were used to investigate how activity was weighing 2% of body weight. Capturing and related to the probability of a binary response collaring conformed to Western Cape Provincial (kill = 1, no kill = 0 and small kill = 0, large kill = 1 ) Government's and American Society of occurring at a GPS cluster. In other words, we Mammalogists' (Gannon ef ai. 2007) guidelines. tested if activity differed significantly over time for Approval was provided by the provincial nature kills or prey size (P-values <0.05 were read as conservation authority Cape Nature (Ethics num- significant). Subsequently, we used the fast ber of the University of Bristol: UB/08/025). The Lomb-Scargle algorithm (Press & Rybicki 1989) to collar was programmed to record 8-24 GPS loca- examine activity data for periodic components tions per day, to partly gain hourly and bi-hourly within each of eight confirmed kill clusters and fixes for more intensive tracking. Apart from eight 'normal activity' periods of at least 45 hours. recording GPS locations, the collar was equipped Intervals of 'normal activity'were chosen as periods with a dual-axis acceleration sensor, recording in which distances between consecutive GPS activity on two axes in 5-minute intervals (for a locations were >200 m. Sinusoidal components of activity data are uncovered by the algorithm and detailed technical description, see Krop-Benesch reflected as amplitudes (Ruf 1999). Consequently, efa/. 2010). 106 South African Journal of Wildlife Research Vol. 42, No. 2, October 2012

Legend ' F10 Kills • F10 — Secondary Roads ELEVATION g500 - 900m ¡901 - 1200m |i201 - 1500m ¡1501 -2100m

Fig. 1. GPS locations and confirmed kill sites of FIO in the period 18 June 2008-27 February 2009 (locations: n = 1760, kills: n= 31). the Lomb-Scargle method calculates levels of killed by F10 consisted of klipspringers (22.6%) statistical significance of peaks in the periodogram and rock hyraxes (55%), diurnal mammal species (Scargle 1982). For the frequency analyses, a living in rocky, rugged terrain (Table 1). Other source period of 24 hours was used. All statistical small antelope (common duiker and Cape analyses were conducted using the open source grysbok) made up a further 9.7% of kills. All prey programming language R (R developmental core species were in the <20 kg class. team 2008). Activity recorded by the dual-axis accelerometer generally showed a decrease with the formation of RESULTS a GPS location cluster. Comparison of activity Within the GPS-cluster study period from June within kill and non-kill clusters revealed no signifi- 2008 to February 2009 (254 days), 1760 GPS cant differences (GAM, /(= 7, P= 0.345). We also positions and approximately 70 000 activity values did not find a significant difference (GAM, /( = 7, were obtained. Seventy-eight potential kill sites P = 0.07) for mean activity when comparing clus- from GPS data records of F10 were identified ters where larger prey (antelope) and smaller prey (Fig. 1). Fifty-four sites were investigated 171 ± (rock hyraxes, rock rabbit) were found. However, 91 days (range = 22-302 d) after the potential activity levels at small-size and large-size kills prédation event, which resulted in the detection of were similar before the kills, but differed from each prey remains at 31 sites (confirmed kill sites). The other after cluster onset. Activity decreased success rate of finding kills using GPS cluster sharply at small-kills sites and stayed low for at analysis was 57.4%. The majority of prey items least seven hours after the presumed time of kill, Fröhlich et al.: GPS cluster analysis and activity data in studies of leopard diet 107

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Fig. 2. Boxplot (median, upper and lower quartile, min and max) of activity counts from leopard F10 before and after cluster initiation, previously averaged over the particular hour relative to cluster onset. Black boxes indicate small prey items (rock hyrax, rock rabbit), grey boxes large kill items (antelope). while activity at large-kill sites increased remark- of their periodic components (Fig. 3). The algo- ably as soon as two hours after the kill - presum- rithm revealed a circadian rhythm with a period ably as a result of eating motion (Fig. 2). length of 24 hours within most 'normal' activity, but The Lomb-Scargle periodogram indicated that not in feeding cluster intervals. In addition, the leopard activity at kill clusters and single positions Lomb-Scargle periodogram detected ultradian differed remarkably in respect to the composition rhythms with a period <24 hours (frequency >1).

Fig. 3. Detection of significant periodic components within confirmed feeding cluster (black bars) and no-cluster (grey bars) sites computed by Lomb-Scargle algorithm. 108 South African Journal of Wildlife Research Vol. 42, No. 2, October 2012

Short-term rhythms were more frequent in feeding Combining the use of GPS locations and activity cluster activity than in normal activity. At frequen- data allowed differentiation between certain cies of 4.5 and 5 (period lengths of 5.3 to 4.8 hours) behavioural categories (Krop-Benesch etal. 2010). the differences were significant (P< 0.05, binomial An increase in leopard activity may be attributed to test), while highly significant (P < 0.01) for this movement between two locations, or movement sample at frequencies of 6.5, 8, 8.5 and 9 (period based at a cluster of locations where the animal is lengths of 3.7, 3, 2.8 and 2.7). not in locomotion. Behaviour where the spatial activity of the animal cannot be deciphered due to DISCUSSION close proximity of GPS locations, or length of time The use of GPS cluster analysis assumes that between locations, may be described using activity predators make a kill, stay for a certain amount of sensor data to include amongst others: hunting, time, forming a cluster of GPS locations, and leave pouncing, dragging of prey, rigorous feeding bouts the cluster after the prey is consumed. It is also when handling larger prey or resting (Krop-Benesch well understood that time spent at a kill site is ef al. 2010). No or very low recorded activity dependent on size of the prey item (e.g. Tambling throughout the duration of clusters suggests these ef a/. 2010; Martins ef a/. 2011). We verified that were probably resting sites (Anderson & Lindzey cluster duration also affected success of locating 2003). Thus, the integrated acceleration sensor kills, as demonstrated by Martins ef al. (2011). provides a helpful means to reject non-kill clusters Because there was a low probability of locating a priori in order to reduce field study efforts. kills at sites occupied for less than 16 hours, these Using GAM, we were unable to differentiate could have included resting sites as well as kill between kill-clusters and non-kill clusters, either sites of smaller prey. We were therefore unable to due to our small sample size and/or the high determine an overall kill rate inclusive of all prey variety of activity at kill sites. However, there was a sizes. Generally, detection of smaller-bodied prey noticeable, although not statistically significant, is crucial in avoiding kill-rate biases towards larger interaction between the activity at small-kill sites prey (Sand ef al. 2005; Webb ef al. 2008; Knopff where rock hyraxes were killed and the activity etal. 2009). Small prey such as rodents or birds is pattern at large-kill sites. As rock hyraxes provide likely to be consumed too quickly to be detected only a fraction of the weight of antelope (Skinner & from GPS locations (however, see Tambling etal. Chimimba 2005), carcasses are consumed effort- (2012) for a method that supplements G PS methods lessly, with leopards remaining at kill site for inter- with scat analyses, potentially reducing the bias mediate periods of time. No distinct feeding activity towards larger prey). However, Martins ef al. pattern characteristic for small-kill sites was found. (2011) demonstrated that the importance of this Killing and handling large prey animals such as type of prey in terms of biomass consumed was an klipspringers needs considerably more time and insignificant part of the diet of leopards in the effort, resulting in distinct activity patterns at kills. Cederberg. The frequency analyses (i.e. the Lomb-Scargle In this study, we explored a new approach of periodogram) demonstrated that large-kill clusters combining GPS cluster analysis and activity data with a long duration were characterized by a obtained from collar-integrated acceleration sensors specific activity pattern that consisted of resting in order to investigate the feeding ecology of intervals with low activity interrupted by feeding leopards in the Cederberg Mountains, South periods of variable length. Little is known about the Africa. At the time of our study, a sufficient GPS, activity and behaviour of leopards at a kill site acceleration and field survey data was only avail- (Bothma & Le Riche 1984; Bailey 1993). We can able for one leopard. Therefore, we did not aim to conclude that the Lomb-Scargle periodogram infer population level behaviour, but to present pilot provides additional means to ascertain leopard data from a new methodological approach to activity while feeding on larger kills, as well, as current GPS cluster techniques. Although only a differentiate kills from non-kills, given that the single female was monitored, our results are con- cluster duration is long enough (>24 hours). sistent with Martins ef al. (2011), who found that Therefore, the appropriate detection of kill sites klipspringers and rock hyraxes formed the majority using this approach is so far limited to larger kills. of the leopard's diet in the Cederberg. Results The method already has been recommended by were corroborated by previously conducted faecal Ruf (1999) for the study of biological rhythms in analyses (Rautenbach 2010; Martins ef a/.2O11 ). telemetrical time-series obtained from free-living Fröhlich et ai.: GPS cluster analysis and activity data in studies of leopard diet 109 animals. In this study, we showed that activity data variables. However, in order to apply the method can be used to provide insight into feeding activity appropriately, smaller GPS fix intervals and a patterns not understood before, and to determine smaller time span between expected kills and duration and frequency of activities such as resting surveys are required. and feeding. Studying leopards in mountainous regions is CONCLUSION challenging (Martins ef ai. 2011) and requires a The purpose of this study was to present pilot data well-conceived implementation of suitable methods. for using a novel approach of combining GPS and Combining different techniques such as GPS/VHF activity data in feeding ecology studies and to telemetry and activity data from acceleration elucidate both advantages and disadvantages of sensors provides helpful means in remote areas using this method. We found that GPS clusters such as the Cederberg Mountains, where direct alone could provide one with certain parameters of observation and scat collections are difficult. How- prédation {e.g. diet preferences, handling time). In ever, detection of kills by combination of activity combination with activity, one could predict kills and location analysis is laborious, as activity data from non-kills, particularly for larger prey. The need to be allocated to the corresponding location method also gives further insight into feeding data. Due to their dissimilar impacts on the collar's patterns when direct observation is improbable battery life, GPS and activity data are obtained at and might serve as a valuable addition to the different intervals. Analyses are further compli- classical GPS cluster investigation approach. cated when GPS fix schedules are irregularly Refining this technique with higher GPS fix rates, changing as a consequence of collar success rate more efficient and timeous collection of kills will or schedule programming. Moreover, technical lead to an improved understanding of feeding limitations of collars and time lags between event patterns for wide-ranging and elusive predators in recording and field visits might have hampered the rugged terrain. utility of GPS data in locating kills. For further study, observations of spatial use, activity and ACKNOWLEDGEMENTS hunting behaviour on more individuals of each age The Rand Merchant Bank Fund, Hans Hoheisen and sex class should be performed to take into Charitable Trust (managed by BoE Private Clients account intra-specific variability. Generally, it is in its capacity as sole Trustee), Leopards Leap advisable that clusters be investigated as soon as Wines, ABAX Foundation, and the Deutsche Bank possible after they occur, because kills will be Africa Foundation sponsored The Cape Leopard easier to locate (Tambling 2010), especially for smaller prey items. Increasing availability of Trust. The Cederberg Conservancy and Cape- real-time GPS data will assist in rapid investigation Nature provided logistical support and gave of clusters (Anderson & Lindzey 2003; Stotyn permission for us to work on their land. We thank 2005). As acknowledged in Martins et ai. (2011), Willem Titus for assistance with field investiga- kills in the Cederberg could have been washed tions. away, covered by vegetation or burnt. In terms of REFERENCES scavengers in the area, there is currently no ANDERSON, C.R. & LINDZEY, F.G. 2003. Estimating evidence of other large predators in the Fynbos cougar prédation rates from GPS location clusters. part of the Cederberg where the home range of the J. Wildl. Manage. 67: 307-316. monitored female was located. Densities of jackal BAILEY, T.N. 1993. The African leopard: ecology and in the Karoo part of our study area must be very behavior of a solitary felid. Columbia University low with fewer than five records since 2004 (Q. Press, New York, U.S.A. BOTHMA, J. du P & LE RICHE, E.A.N. 1984. Aspects of Martins, pers. comm.). Therefore, bone fragments the ecology and behaviour of the leopard {Panthera would still remain. Despite possible biases, kills pardus) in the Kalahari desert. Koedoe27:259-279. found still demonstrate diet based on clusters COULOMBE, M-L., MASSÉ, A. & CÔTE, S.D. 2006. analysis. We were able to locate kill remains for up Quantification and accuracy of activity data to 3.5 years after the kill event (Martins etal. 2011 ). measured with VHF and GPS telemetry Wildl. Soc. In addition, the interval between the kill and Bu//. 34:81-92. DEMMA, D.J., BARBER-MEYER, S.M. & MECH, LD. searching for it was not significant in predicting 2007. Testing global positioning system telemetry to whether a kill was found regardless of how this study wolf prédation on deer fawns. J. Wildl. Manage. variable was included in the set of explanatory 71:2767-2775. ESRI (Environmental Systems Research Institute, Inc.) 110 South African Journal of Wildlife Research Vol. 42, No. 2, October 2012

2010. ArcGIS Version 9.2. Environmental Systems AVERY, G. 1986. Prey of leopards in four mountain- Research Institute, Inc., Redlands, CA, U.S.A. ous areas of the south-western Cape Province. GANNON, W.L., SIKES, R.S. & Animal Care and Use S. Afr J. Wiidi. Res. 16: 47-52. Committee of the American Society of Mammai- PRESS, W.H. & RYBICKI, G.B. 1989. Fast algorithm ogists. 2007. Guidelines of the American Society of for spectral analysis of unevenly sampled data. Mammalogists for the use of wild mammals in re- Astrophys. J. 338: 277-280. search. J. Mammal. 88: 809-823. R DEVELOPMENT CORE TEAM. 2008. R: a language GERVASI, V., BRUNBERG, S. & SWENSON, J.E. 2006. and environment for statistical computing, 2.7.0. R An individual-based method to measure animal Foundation for Statistical Computing, Vienna, Aus- activity levels: a test on brown bears. Wiidi. Soc. Buil. tria. Online at: http://www.R-project.org/ (accessed 34:1314-319. 15 September 2009). JENNY, D. & ZUBERBÜHLER, K. 2005. Hunting behav- RAUTENBACH, T. 2010. Assessing the diet of the Cape iour in West African forest leopards. Afr J. Ecoi. 43: leopard (Panthera pardus) in the Cederberg and 197-200. Gamka Mountains, South Africa. M.Sc. thesis. KNOPFF, K.H., KNOPFF, A.A., WARREN, M.B. & Nelson Mandela Metropolitan University, Port Eliza- BOYCE, M.S. 2009. Evaluating global positioning beth, South Africa. system telemetry techniques for estimating cougar RUF, T. 1999. The Lomb-Scargle periodogram in prédation parameters. J. Wildl. Manage. 73:586-597. biological rhythm research: analysis of incomplete KNOPFF, K.H., KNOPFF, A.A., KORTELLO, A. & and unequally spaced time-series. Bioi. Rhythm Res. BOYCE, M.S. 2010. Cougar kiii rate and prey compo- 30: 178-201. sition in a multiprey system. J. Wiidi. Manage. 74: SAND, H.B., ZIMMERMANN, B., WABAKKEN, H.A. & 1435-1447. PEDERSEN, H.C. 2005. Using GPS technology KROP-BENESCH, A., BERGER, A, STREICH, J. & and GIS cluster analyses to estimate kill rates in SCHEIBE, K. 2010. Activity Pattern User's Manual. wolf-ungulate ecosystems. Wiidi. Soc. Bull. 33: Vectronic Aerospace, Berlin, Germany. Oniine at: 914-925. http://www.vectronic-aerospace.com SCARGLE, J.D. 1982. Studies in astronomical time LE ROUX, PG. & SKINNER, J.D. 1989. A note on the series analysis. II. Statistical aspects of spectral ecology of the leopard (Panifiera pardus Unnaeus) in analysis of unevenly spaced data. Astrophys. J. 302: the Londolozi Game Reserve. Afr. J. Ecoi. 27: 757-763. 167-171. SKINNER, J.D. & CHIMIMBA, C.T 2005. The mammals LÖTTKER, P, RUMMEL, A., TRAUBE, M., STÄCHE, A., of the southern African subregion, 3rd edn. Cam- SUSTR, P, MULLER, R & HEURICH, M. 2009. New bridge University Press, Cape Town, South Africa. possibilities of observing animal behaviour from a STOTYN, S. 2005. Determining wolf summer prédation distance using activity sensors in GPS-coilars: an patterns using GPS cluster analysis: a preliminary attempt to calibrate remotely collected activity data report. Department of Renewable Resources, with direct behavioural observations in red deer University of Alberta, Edmonton, Canada. Cervus elaphus. Wiidl. Bioi. 15: 425-434. TAMBLING, C.J., CAMERON, E.Z., DU TOIT J.T & MARTINS, 0.2010. The ecology of the leopard Panthera GETZ, W.M. 2010. Methods for locating african lion pardus in the Cederberg Mountains. PhD disserta- kills using Global Positioning System movement tion. University of Bristol, U.K. data. J. Wildl. Manage. 74: 549-556. MARTiNS, O., HORSNELL, W.G.C., TITUS, W., TAMBLING, C.J., LAURENCE, S.D., BELLAN, S.E., RAUTENBACH, T & HARRIS, S. 2011. Diet determi- CAMERON, E.Z., DU TOIT J.T & GETZ, W.M. 2012. nation of the Cape Mountain leopards using global Estimating carnivoran diets using a combination of positioning system location clusters and scat analy- carcass observations and scats from GPS clusters. sis. J. Zoo/. 283: 81-87. J. Zoo/. 286: 102-109. MERRILL, E., SAND, H., ZIMMERMANN, B., McPHEE, WEBB, N.F, HEBBLEWHITE, M. & MERRILL, E.H. H.WEBB, N., HEBBLEWHITE, M., WABAKKEN, P& 2008. Statisticai methods for identifying wolf kill sites FRAIR, J.L. 2010. Building a mechanistic under- using global positioning system locations. J. Wildl. standing of prédation with G PS-based movement Manage. 72: 798-807. data. Phil. Trans. Roy Soc. B Bioi. Sei. 365: 2279- WOOD, S.N. 2006. Generalized additive models: an 2288. introduction with R. Chapman and Hall/CRC, NORTON, PM., LAWSON, A.B., HENLEY, S.R. & London.

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