LIMPOPO LEOPARD MONITORING REPORT CAMERA-TRAP SURVEY 2016 DINOKENG

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1 LIMPOPO LEOPARD MONITORING REPORT CAMERA-TRAP SURVEY 2016 DINOKENG

2 LIMPOPO LEOPARD MONITORING PROJECT DINOKENG CAMERA-TRAP SURVEY 2016 Ross Pitman*, Gareth Mann, Gareth Whittington-Jones, Lisa Thomas & Guy Balme * rpitman@panthera.org (Corresponding author) INTRODUCTION The Limpopo Leopard Monitoring Project aims to provide robust data on leopard population trends in Limpopo Province to inform conservation policy and management. Here we report on a camera-trap survey undertaken in Dinokeng Game Reserve (hereafter Dinokeng). This is the first annual survey for Dinokeng. METHODS We deployed paired camera stations were setup at 36 locations for a total of 48 days. To ensure all individuals within the sampled areas had a probability > 0 of being captured, camera-traps were distributed an average of 2 3 km from one another. To maximize the probability of photographing leopards, camera-traps were placed in high-use areas, such as drainage lines, animal paths, and roads. Camera-traps were mounted on trees or steel poles located 2 4 meters from the focal movement pathway. To reduce false photographic captures, we cleared any vegetation that might obstruct the camera-trap s field of view. Camera-traps were not moved during the surveys. Camera-trap images were catalogued using camtrapr (Niedballa et al. 2016), within the R Statistical Environment (R Core Team 2015). We identified individuals based on their unique pelage patterns within the pattern recognition software, Wild-ID (Bolger et al. 2012). In addition, all computer-assisted identifications were manually verified. Bayesian spatially-explicit capture-recapture models We followed the capture re-capture analytical methods, and hierarchical model formulation, described by Goldberg et al. (Goldberg et al. 2015) and Royle et al. (Royle et al. 2009). The model relates the observations, y ijk, of individual i in trap j during sampling interval k to the latent distribution of activity centers. Observation, y ijk, took the value of one for a capture, and zero if not captured, to produce a 2 P age

3 capture history for all individuals in all traps over all sampling intervals. Multiple detections of the same individual, within the same sampling period, were taken as a single capture. Individuals could be captured on multiple traps during a sampling interval (24 hours). We followed the formulation of the observation process used by Goldberg et al. (Goldberg et al. 2015), Gardner et al. (Gardner et al. 2010), and Russell et al. (Russell et al. 2012). Our spatially-explicit capture-recapture models were implemented within a Bayesian framework using data augmentation (Royle & Young 2008; Goldberg et al. 2015). Data augmentation adds a sufficiently large number of all-zero capture histories to create a dataset of size M individuals (Goldberg et al. 2015). Augmentation was considered large enough when the number of augmented individuals did not truncate the posterior estimates of population size (Goldberg et al. 2015; Proffitt et al. 2015). Data augmentation in this study was set to 400. We chose a uniform prior distribution from 0 to M on population size (Goldberg et al. 2015). Starting values for parameters were: s = 1, q = 0.75, ln(a 0 ) = 0, b = 0, Y = 0, Y sex = proportion of males sampled. We used improper priors (-, ) for a 0 and all b parameters, (0, ) for s, (0.5, 1) for q, and (0, 1) for Y and Y sex. Models were fit using Markov chain Monte Carlo (MCMC) methods within R, using the SCRbayes package (available at: To account for individual, sex-specific effects, we included a sex covariate within all models. Although cubs (< 12 months old) were occasionally captured on the camera-traps, we only included adults and sub-adults within our analyses. To account for heterogeneity in habitat use across the study area, we modelled our density estimate using an existing resource selection function (Pitman et al. in press) as a density covariate (Royle & Chandler 2013; Proffitt et al. 2015). All analyses were run using a statespace of 20 km. Models were run for 50,000 iterations, with a burn-in of 10,000. To reduce autocorrelation, we thinned the MCMC chains by skipping every other iteration, resulting in 12,500 iterations in our posterior sample. We evaluated model goodness of fit using a standard Bayesian P-value approach (Royle et al. 2013). Convergence of the MCMC chains were assessed by examining posterior parameter-wise traceplots and histograms. The mean and 95% credibility intervals, for each model parameter, were then computed from these converged samples (Goldberg et al. 2015). In addition to estimating population density, we assessed the demographic composition of the sampled population. We estimated the age and sex of captured leopards using their relative body dimensions, the presence of a well-developed dewlap, and facial scarring (Balme et al. 2012). We classified leopards into 3 P age

4 three age classes: juveniles ( 2 years), subadults (>2 years; 3 years) and adults (>3 years). For adult males, we distinguished between individuals <7 years and 7 years. RESULTS Camera-trap surveys: The total area covered by camera-trap stations at Dinokeng amounted to km 2. The survey ran from the 02 July 2016 to the 18 August 2016, and sampling effort comprised 2,968 camera-trap nights. A total of 81,109 photographs were recorded, of which 62,222 were independent captures (this includes duplicates, blank photos, and photos of the research team). A total of 58 different species were recorded (see Appendix 1 for a summary). Unfortunately, leopards were not photographed at any camera stations across Dinokeng, and suggests that no resident leopard population exists on the reserve. Other medium and large carnivores were, however, captured on numerous occasions. Lion were captured on 129 occasions (Fig. 1) across Dinokeng, whilst brown hyaena were captured on 49 occasions (Fig. 2) predominantly within the northern-western section of the reserve. Cheetah were infrequently captured on Dinokeng (n = 3; Fig. 3), but this is in large part a result of our camera-trapping array, which was not designed to photograph cheetah. Caracal and serval were captured fairly frequently on Dinokeng, amounting to 19 (Fig. 4) and 52 (Fig. 5) captures, respectively. Dinokeng Game Reserve Captures Dinokeng_ P age

5 Figure 1. Lion capture frequencies recorded at camera-trap stations in Dinokeng during the 2016 survey. Larger circles indicate greater lion activity. Dinokeng Game Reserve Captures Dinokeng_ Figure 2. Brown hyaena capture frequencies recorded at camera-trap stations in Dinokeng during the 2016 survey. Larger circles indicate greater brown hyaena activity. 5 P age

6 Dinokeng Game Reserve Captures Dinokeng_ Figure 3. Cheetah capture frequencies recorded at camera-trap stations in Dinokeng during the 2016 survey. Larger circles indicate greater cheetah activity. Dinokeng Game Reserve Captures Dinokeng_ Figure 4. Caracal capture frequencies recorded at camera-trap stations in Dinokeng during the 2016 survey. Larger circles indicate greater caracal activity. 6 P age

7 Dinokeng Game Reserve Captures Dinokeng_ Figure 5. Serval capture frequencies recorded at camera-trap stations in Dinokeng during the 2016 survey. Larger circles indicate greater serval activity. As the primary leopard prey species, impala and kudu were captured throughout the reserve, and on 1,202 (Fig. 6) and 383 occasions (Fig. 7), respectively. 7 P age

8 Dinokeng Game Reserve Captures Dinokeng_ Figure 6. Impala capture frequencies recorded at camera-trap stations in Dinokeng during the 2016 survey. Larger circles indicate greater impala activity. Dinokeng Game Reserve Captures Dinokeng_ Figure 7. Kudu capture frequencies recorded at camera-trap stations in Dinokeng during the 2016 survey. Larger circles indicate greater kudu activity. 8 P age

9 Analyses of activity patterns indicate that lions on Dinokeng are active during nocturnal hours, with peaks at crepuscular periods (Fig. 8) with a noticeable peak during early morning hours. In contrast, impala avoided moving around during nocturnal hours, and rather opted to remain active during daylight hours (Fig. 9). Caracal and serval appeared active during similar times, which is clearly evident in the large degree of temporal overlap (grey regions; Fig. 10). Lion and brown hyaena similarly exhibited large degrees of temporal overlap, though brown hyaena clearly favoured nocturnal hours, whilst lion showed a peak of activity during post-dawn hours (Fig. 11). Activity of Lion number of records: 129 Density :00 6:00 12:00 18:00 24:00 Time Figure 8. Lion activity patterns in Dinokeng during the 2016 survey. Higher peaks indicate increased activity, whilst troughs indicate periods of lower activity. 9 P age

10 Activity overlap: Lion Impala number of records: 129 / 1202 Density Lion Impala Dhat1=0.4 0:00 6:00 12:00 18:00 24:00 Time Figure 9. Lion and impala activity patterns in Dinokeng during the 2016 survey. Higher peaks indicate increased activity, whilst troughs indicate periods of lower activity. Grey shading indicates temporal overlap between these two species. Caracal Serval Activity overlap: Caracal Serval number of records: 19 / 52 Dhat1=0.82 Density :00 6:00 12:00 18:00 24:00 Time 10 P age

11 Figure 10. Caracal and serval activity patterns in Dinokeng during the 2016 survey. Higher peaks indicate increased activity, whilst troughs indicate periods of lower activity. Grey shading indicates temporal overlap between these two species. Activity overlap: Lion Brown_Hyaena Density Lion Brown_Hyaena number of records: 129 / 49 Dhat1=0.76 0:00 6:00 12:00 18:00 24:00 Time Figure 11. Lion and brown hyaena activity patterns in Dinokeng during the 2016 survey. Higher peaks indicate increased activity, whilst troughs indicate periods of lower activity. Grey shading indicates temporal overlap between these two species. DISCUSSION The Limpopo Leopard Monitoring Project continues to fulfil its mandate to provide robust data on leopard population density and trends, which can inform management decisions. However, it is quite alarming to conclude that leopards are largely absent on Dinokeng. It s important to mention that, although this survey found no evidence of an established leopard population, that s not to mean that leopards do not traverse the reserve. It is highly likely that opportunistic sightings of leopard on Dinokeng are that of dispersing individuals. The high degree of available prey on Dinokeng suggest that the reserve is potentially capable of supporting leopards. However, the high density of human communities around the reserve might act to exclude leopards from this region especially given that leopards are not bound by game fences, and therefore freely move into areas best suited for their survival. The prolonged drought in this region of South Africa much like the rest of southern Africa may have influenced leopard 11 P age

12 movement patterns, but we would have still expected to detect leopards (even at very low densities). Indeed, droughts often represent optimum periods for predators, due to the poor condition of prey and increased scavenging opportunities (Owen-Smith & Mills 2006); therefore, the lack of leopard activity on Dinokeng is particularly alarming. Illegal killing of leopards, both incidentally through bushmeat poaching (Henschel et al. 2011), intensive game ranching practices (Pitman et al. 2016), or deliberately for their pelts and body parts, is very likely playing a role in depressing leopard populations across the region. It therefore remains to be seen whether Dinokeng s leopard population is capable of recovery. Across much of their range in South Africa, leopard population numbers are decreasing. As a result, no leopard trophy hunting permits have been issued in South Africa for The absence of this source of additional mortality may allow some degree of population recovery in the near future. ACKNOWLEDGEMENTS We are grateful to all the staff on Dinokeng Game Reserve for allowing access and WEI for providing support to the project. REFERENCES Bolger, D.T., Morrison, T.A., Vance, B., Lee, D. & Farid, H. (2012). A computer-assisted system for photographic mark-recapture analysis. Methods Ecol Evol, 3, Gardner, B., Royle, J.A., Wegan, M.T., Rainbolt, R.E. & Curtis, P.D. (2010). Estimating Black Bear Density Using DNA Data From Hair Snares. J. Wildl. Manage., 74, Goldberg, J.F., Tempa, T., Norbu, N., Hebblewhite, M., Mills, L.S., Wangchuk, T.R. & Lukacs, P. (2015). Examining Temporal Sample Scale and Model Choice with Spatial Capture-Recapture Models in the Common Leopard Panthera pardus. PLOS ONE, 10, e Henschel, P., Hunter, L.T.B., Coad, L., Abernethy, K.A. & Mühlenberg, M. (2011). Leopard prey choice in the Congo Basin rainforest suggests exploitative competition with human bushmeat hunters. J. Zool., 285, Niedballa, J., Sollmann, R., Courtiol, A. & Wilting, A. (2016). camtrapr: an R package for efficient camera trap data management. Methods Ecol Evol, Pitman, R.T., Fattebert, J., Williams, S.T., Williams, K.S., Hill, R.A., Hunter, L.T.B., Slotow, R. & Balme, G.A. (2016). The conservation costs of game ranching. Conservation Letters, Proffitt, K.M., Goldberg, J.F., Hebblewhite, M., Russell, R.E., Jimenez, B.S., Robinson, H.S., Pilgrim, K. & Schwartz, M.K. (2015). Integrating resource selection into spatial capture-recapture models for large carnivores. Ecosphere, 6, R Core Team. (2015). R: a language and environment for statistical computing. Royle, J.A. & Chandler, R.B. (2013). Integrating resource selection information with spatial capturerecapture. Methods Ecol Evol, 4, Royle, J.A. & Young, K.V. (2008). A hierarchical model for spatial capture-recapture data. Ecology, 89, P age

13 Royle, J.A., Chandler, R.B., Sollmann, R. & Gardner, B. (2013). Fully spatial-recapture models. Academic Press, New York, New York, USA. Royle, J.A., Karanth, K.U., Gopalaswamy, A.M. & Kumar, N.S. (2009). Bayesian inference in camera trapping studies for a class of spatial capture recapture models. Ecology, 90, Russell, R.E., Royle, J.A., Desimone, R., Schwartz, M.K., Edwards, V.L., Pilgrim, K.P. & McKelvey, K.S. (2012). Estimating abundance of mountain lions from unstructured spatial sampling. Jour. Wild. Mgmt., 76, APPENDIX 1 SUMMARY OF CAPTURE DATA COMMON NAME NUMBER OF CAPTURES AARDVARK 56 AARDWOLF 13 AFRICAN_CIVET 6 BANDED_MONGOOSE 12 BAT 1 BIRD 682 BLACK-BACKED_JACKAL 667 BLUE_WILDEBEEST 970 BONTEBOK 69 BROWN_HYAENA 49 BUFFALO 51 BUSHBUCK 25 BUSHPIG 5 CANE_RAT 4 CARACAL 19 CHEETAH 3 COMMON_DUIKER 137 DOG 8 DOMESTIC_CAT 6 DWARF_MONGOOSE 3 ELAND 50 ELEPHANT 55 GENETTA 5 GIRAFFE 549 GREATER_BUSHBABY 1 HARTEBEEST 81 HIPPOPOTAMUS 4 HONEY_BADGER 24 IMPALA P age

14 INSECT 24 KUDU 383 LARGE-SPOTTED_GENET 11 LION 129 MELLERS_MONGOOSE 8 NILE_MONITOR 2 NYALA 45 PORCUPINE 278 RESEARCH_TEAM 648 SCRUB_HARE 455 SERVAL P age