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Carnivore distributions in Botswana are shaped by availability and intraguild

Article in Journal of Zoology · May 2017 DOI: 10.1111/jzo.12470

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Carnivore distributions in Botswana are shaped by resource availability and intraguild species L. N. Rich1, D. A. W. Miller2, H. S. Robinson3,4, J. W. McNutt5 & M. J. Kelly1

1 Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, USA 2 Department of Science and Management, Penn State, University Park, PA, USA 3 College of Forestry and Conservation, University of Montana, Missoula, MT, USA 4 Panthera, New York, NY, USA 5 Botswana Predator Conservation Trust, Maun, Botswana

Keywords Abstract camera trap; carnivore; dynamics; intraguild species; occupancy modeling; seasonal The composition of ecological communities is shaped by the interplay between distributions. interspecific interactions and species’ and requirements. The influence of interspecific interactions is particularly widespread within carnivore guilds, Correspondence where species must balance the trade-off between resource acquisition and poten- Lindsey N. Rich, Department of Fish and Wildlife tially costly encounters with intraguild competitors/predators. We tested whether Conservation, Virginia Tech, 318 Cheatham Hall, intraguild species activity or resource availability had a stronger influence on the Blacksburg, VA 24061-0321, USA. seasonal distributions of 10 carnivore species in northern Botswana. We deployed Email: [email protected] 132 camera stations across a 330 km2 area during the 2014 dry season and 2015 wet season. For each species and season, we developed occupancy models based Editor: Matthew Hayward on resource availability (i.e. prey, vegetation and water) and on intraguild species (i.e. photographic detection rates of intraguild competitors and predators). We then Received 16 June 2016; revised 29 March 2017; used k-fold cross-validation to assess the relative predictive ability of each model. accepted 4 April 2017 Carnivore distributions were generally negatively associated with dense vegetation and contrary to expectations, positively associated with the detection rate of intra- doi:10.1111/jzo.12470 species. This suggests competitor/predator avoidance did not play a large role in influencing carnivore distributions in northern Botswana, a result that differs from other systems, and might be attributed to differences in habitat, carnivore den- sities, and prey availability. The predictive ability of our resource availability vs. intraguild species models differed between seasons and among species. distributions, for example, were best predicted by resource availability during the dry season and by the activity of intraguild species in the wet season. The majority of seasonal distributions were best predicted by intraguild species activity or a combination of both resource availability and intraguild species activity. As envi- ronments continually change, studies similar to ours are pertinent as they can be used to monitor distributions of wildlife communities and to better understand the relative importance of the diversity of ecological processes impacting wildlife com- munities.

Introduction Such information is especially pertinent for mammalian carni- vores as they are wide ranging, sensitive to environmental and Carnivore populations have declined globally due to increasing land use changes, and generally exist at low densities, making populations, widespread habitat loss, and declining prey them particularly vulnerable to local extinctions (Woodroffe & populations (Ripple et al., 2014; Bauer et al., 2015). African Ginsberg, 1998; Bauer et al., 2015). wild (Lycaon pictus), tigers (Panthera tigris), and The composition of carnivore communities is shaped by the (Panthera leo), for example, have each disappeared from interplay between competitive interactions, and species’ habitat >80% of their historical range (Ripple et al., 2014; Bauer and food requirements (Rosenzweig, 1966; Holt, 1984). Com- et al., 2015). As the extent and quality of wildlife habitat petitive interactions are particularly widespread within the car- erodes, information on the distributions of remaining carnivore nivore community, influencing individual behavior, population populations is needed to facilitate informed conservation dynamics, and community structure (Rosenzweig, 1966; Palo- actions aimed at mitigating declines (Pettorelli et al., 2010). mares & Caro, 1999; Caro & Stoner, 2003). Typically,

Journal of Zoology (2017) – ª 2017 The Zoological Society of London 1 Carnivore distributions in Botswana L. N. Rich et al. increases as carnivore species become more similar availability, whereas the distributions of meso-carnivores would in habitat selection, diet, activity pattern, and body size be influenced by both resource availability and the activity of (Rosenzweig, 1966). Interspecific killing (i.e. killing with or intraguild species (Palomares & Caro, 1999; Heithaus, 2001). without consumption), alternatively, is more likely among car- By evaluating the relative importance of the diversity of eco- nivore species with body sizes that differ by a factor of 2–5.4 logical processes impacting carnivore communities, our and with overlapping diets (Donadio & Buskirk, 2006). The research aims to inform the conservation of these wildlife pop- potential for intraguild interactions can result in spatial and ulations in sub-Saharan . temporal niche partitioning among species, with subordinate species often changing their habitat use to less optimal areas in Materials and methods an effort to minimize encounters with dominant species (Creel & Creel, 1996; Durant, 1998). This can result in top predators Study area matching the distributions of their prey, while subordinate predators balance the trade-off between resource acquisition Our study was carried out in Ngamiland District of northern and risk of aggression and from larger preda- Botswana. The dominant land covers included floodplains, tors (Palomares & Caro, 1999; Heithaus, 2001). In East Africa, , and mopane shrub (Colophospermum mopane) and for example, cheetahs (Acinonyx jubatus) and wild dogs, both woodlands. Our study site encompassed the eastern section of subordinate competitors, occurred in areas with lower prey Moremi Game Reserve and parts of wildlife management areas densities than areas frequented by lions and spotted hyenas NG33/34 (Fig. 1). Wildlife was fully protected within Moremi (Crocuta crocuta), the dominant competitors (Creel & Creel, Game Reserve and partially protected within the wildlife man- 1996; Durant, 1998). agement areas. Both areas were used for photographic tourism When modeling intraguild interactions and community (i.e. no hunting or ). The region has two dis- dynamics, it is also vital to account for species’ decisions tinct seasons, dry and wet, with rain (~300–600 mm/year) fall- regarding habitat use, as habitat selection plays an important ing almost exclusively during the wet season, which generally role in determining and community struc- lasts from November to April. ture (Brown, Laundre & Gurung, 1999; Heithaus, 2001). Prey density, for example, has been found to be a fundamental dri- Camera trap survey ver of carnivore densities and distributions (Carbone & Gittle- man, 2002) as has water availability, with carnivore occupancy We deployed camera stations at 132 locations across a 330 km2 often increasing close to permanent water sources (Pettorelli area between August and November 2014 (dry season) and again et al., 2010; Schuette et al., 2013). Vegetation cover and pro- between February and May 2015 (wet season). Each camera sta- ductivity may also influence carnivore distributions, often due tion included two opposing cameras, offset by 0.5–1 m. The to their relationships with prey (Pettorelli et al., majority of cameras were Panthera v4 incandescent-flash camera 2005). traps (0.18 s trigger speed) but we also used several Bushnell The mammalian carnivore community in northern Botswana TrophyCam infra-red camera traps (0.3 s trigger speed). To keep is one of the most diverse assemblages of carnivores in Africa detection rates comparable among stations, we ensured every (Caro & Stoner, 2003). The strength and frequency of interac- camera station included at least one Panthera camera trap. We tions within this carnivore guild likely varies based on resource mounted cameras on trees and if no trees were available, on availability, the respective species’ resource requirements, and metal fence posts hammered into the ground. We secured cam- their rank within the intraguild hierarchy (Rosen- eras at knee height and positioned cameras to photograph flanks zweig, 1966; Palomares & Caro, 1999; Heithaus, 2001; Caro of passing . We programmed cameras to take three pho- & Stoner, 2003). Seasonality may also play a role as the tos at each trigger event in the daytime with a delay of 30 s region has distinct wet and dry seasons, resulting in pro- between trigger events. At nighttime, the infra-red cameras took nounced differences in vegetation that can influence, for exam- three photos when triggered while the incandescent-flash cameras ple, prey availability. took one photo every 15 s due to the flash having to re-charge. In this study, we tested whether intraguild species activity or We treated each station as a single data point by combining resource availability had a stronger influence on the seasonal information from the two opposing cameras using the time/date distributions of 10 carnivore species in northern Botswana. For stamps on the photographs (e.g. if a leopard was recorded by each species and season, we developed occupancy models both cameras at the same time on the same date it was only based on variables pertaining to resource availability (i.e. prey, recorded once). vegetation and water) and to the activity of intraguild competi- We used 5-km2 grid cells to guide the placement of cameras tors and predators. We then used k-fold cross-validation to and ensure systematic coverage of the study area. Exploratory assess the relative predictive ability of each model. We research within our study area, where cameras were placed expected carnivore distributions to have a stronger, negative randomly, found detection rates of wildlife species were pro- association with their dominant predators’ activity, as encoun- hibitively low. Thus, we placed all cameras on sand, 4-wheel ters with these species have a higher associated risk than drive roads to increase our probability of photographing carni- encounters with similar-sized competitors (Donadio & Buskirk, vores given that large predators and feeders often use 2006). Overall, we predicted the distributions of large carni- lightly traveled roads as movement corridors (Forman & vores would be predominantly influenced by resource Alexander, 1998). We deployed two camera stations (i.e. 4

2 Journal of Zoology (2017) – ª 2017 The Zoological Society of London L. N. Rich et al. Carnivore distributions in Botswana

Figure 1 Our camera trap survey was carried out in the eastern section of Moremi Game Reserve and wildlife management areas NG33/34, Botswana, 2014–2015. cameras) within each grid cell, one on the road closest to the (i.e. Enhanced Vegetation Index- EVI) may influ- predetermined center point of each grid cell and the second on ence the seasonal occupancy patterns of carnivore species. To the road closest to a predetermined random point within each represent relative prey availability, we used a species-specific grid cell. We used a rotational system for camera deployment surrogate. For each camera station, we estimated the kilograms where we divided our study area into three, ~110 km2 sub- of prey per trap day by multiplying the number of areas and sequentially sampled each area for 30 days. We independent detections of a prey species by its average adult deployed an average of 44 camera stations within each sub- body mass (based on Estes, 1991) and standardized this value area. by sampling effort (i.e. number of days the camera station was To test whether our camera locations represented a random active). Independent detections of a prey species were defined sample of the landscape, we compared them to an equal num- as photo events separated by ≥30 min unless different individ- ber of randomly generated points in our study area. We used a uals could be distinguished. We then summed biomass values chi-square goodness of fit test to compare the proportion of for each prey species of the respective carnivore to obtain our camera locations falling within five land cover types, as deter- camera station and species-specific estimates of prey biomass. mined by a land classification map for the region (Bennitt, We included prey weighing <5 kg for African civets (Civettic- Bonyongo & Harris, 2014), to expected proportions based on tis civetta), wildcats (Felis silvestris), and (Leptailurus randomly generated points. We found no difference between ), <20 kg for black-backed jackals (Canis mesomelas), the proportion of habitat sampled by randomly generated ( caracal), and honey (Mellivora points and our camera locations (v2=0.10, d.f. = 4, P- capensis), <100 kg for leopards (Panthera pardus), 10–60 kg value = 0.99), supporting that our camera locations represented for wild dogs, and ≥100 kg for lions and spotted hyenas a random sample of the landscape. (Appendix S1). The prey species included for each carnivore were based on Estes (1991) and kill site data from the Bots- wana Predator Conservation Trust (unpublished data). We Covariates based our measure of relative prey availability on detection rates because it provided more detailed information regarding Resource availability how frequently a prey species used the area surrounding a We hypothesized resource availability, including relative prey camera station than would estimates of prey occupancy. For availability, vegetative cover, water, and vegetation example, the number of independent detections of

Journal of Zoology (2017) – ª 2017 The Zoological Society of London 3 Carnivore distributions in Botswana L. N. Rich et al.

(Aepyceros melampus) at a camera ranged from 0 to 297. camera station. To represent vehicle activity, we determined Detection rates capture this variability, whereas an occupancy the mean number of photo events of vehicles per trap day for analysis would not. We note that we placed cameras on roads the respective camera station. to maximize carnivore detections and hence, we may have underrepresented relative prey availability. Modeling framework The dominant vegetative covers found within our study area included open floodplains/grasslands and densely vegetated We fit single season, single species occupancy models in a mopane shrub and woodlands. We included mopane cover in Bayesian framework to evaluate the seasonal distributions of our analysis. We used a land classification map (Bennitt et al., all meso- and large carnivores in our study area (Appendix S2; 2014) to calculate per cent mopane cover within a 1 km buf- MacKenzie et al., 2005). We defined occurrence as the proba- fered area surrounding each camera station. A 1 km buffer size bility an used the area sampled by a camera station provides information on the general conditions surrounding the during our survey period (MacKenzie et al., 2005). To account camera station applicable to our suite of variably sized species. for incomplete detection, we treated each trap day as a repeat We estimated distance to water by calculating the distance survey at a particular camera station; a trap day was each 24- (km) from each camera station to the nearest permanent water hr time period lasting from midnight to midnight. Our detec- source. Lastly, we included season-specific estimates of EVI. tion histories then consisted of camera station-specific counts By measuring infrared reflectance, this satellite-derived mea- of the number of trap days during which the respective carni- sure provides information on the spatial and temporal distribu- vore species was photographed. We did not use a multi-season tion of vegetation productivity (Pettorelli et al., 2005). We model because we wanted to limit the number of parameters downloaded monthly 30 m resolution MODIS EVI datasets and because our research was focused on occupancy probabili- from the USGS and NASA land processes archive center ties, as compared to local colonization and extinction probabili- (https://lpdaac.usgs.gov/). We used the median values from ties. We evaluated four models per species per season, a null August through October to represent the dry season and the (i.e. included detection covariates only), resource availability, median values from February through April for the wet season intraguild species, and all-inclusive (i.e. all covariates com- as these were the months when the majority of our sampling bined) occurrence model. We tested for correlations (r >|0.6|) took place. among all covariates using Pearson correlation coefficients (Dormann et al., 2013) and found no evidence of correlations. Intraguild species We limited our analysis to these four models given our objec- tive was testing whether resource availability or the activity of We hypothesized the activity of intraguild species, as repre- intraguild species had a larger influence on the seasonal distri- sented by the detection rate of intraguild competitors and butions of carnivores. Our resource availability occurrence (w) predators, may influence the occupancy patterns of carnivore model for the respective species at camera station j was speci- species. For each season and camera station, we standardized fied as: the number of independent detections (photo events separated ≥ ðw Þ¼a þ a þ a by 30 min) of a carnivore species by sampling effort. Similar logit j 0 1(prey availability)j 2(mopane cover)j þ a þ a to prey availability, we used detection rates as they capture 3(water)j 4(EVI)j greater variability among sites than occupancy. For each or our ’ study s target carnivores, we then summed the camera station- the intraguild species occurrence model was specified as: specific values for the species designated as competitors or ðw Þ¼a þ a þ a dominant predators (Appendix S2). We designated species that logit j 0 1(competitors)j 2(predators)j were similar in weight and diet as competitors and species that were ≥twice the size of the target carnivore as dominant preda- and the detection (p) model associated with all occurrence tors. models was: ð Þ¼b þ b logit pj 0 1(vegetation density)j Detection probability þ b 2(human/vehicle activity)j Finally, for detectability, we hypothesized that seasonal vegeta- tion density and vehicle activity may influence a species’ prob- We standardized all covariates to have a mean of 0 and ability of being photographed (i.e. detection probability). To standard deviation of 1. Our modeling approach did not measure the vegetation density of the area immediately sur- account for spatial autocorrelation. We do not believe, how- rounding the camera station during each season, we took pho- ever, that spatial autocorrelation had a large influence on our tographs at knee height, one pointed at 90 and the other at results given the extent of our sampling and the localized scale 270 in relation to the road. We took these photos at the cam- of our inferences (i.e. habitat use vs. home range scale). era station, 50 m up the road and 50 m down the road. We We estimated posterior distributions of parameters using then digitally placed a 13 9 15 grid over each photo and Markov chain Monte Carlo (MCMC) simulation implemented determined the proportion of grid cells that were ≥50% cov- in JAGS (version 3.4.0) through program R (R2Jags; Plummer, ered by forbs, shrubs, or trees and averaged this across the six 2011). We generated three chains of 100 000 iterations after a photos as our estimate of vegetation density for the respective burn-in of 20 000 and thinned by 10. We used non-

4 Journal of Zoology (2017) – ª 2017 The Zoological Society of London L. N. Rich et al. Carnivore distributions in Botswana informative priors including a uniform distribution of 0 to 1 seasons (w ≥ 0.9), whereas black-backed jackals and servals on the real scale for a0 and b0 and uniform from 0 to 10 for had more restricted distributions (w < 0.5; Fig. 2). African standard deviation parameters. We used a normal prior distri- civets and honey were the only species to show differ- bution with a mean of 0 and standard deviation of 100 on the ences (i.e. 95% credible intervals, CI, did not overlap) in their logit-scale for the remaining covariate effects. We assessed estimated occupancy probabilities between seasons, with distri- convergence using the Gelman-Rubin statistic where values butions becoming more restricted in the wet season (Fig. 2). <1.1 indicated convergence (Gelman et al., 2004). The direction and strength of covariate effects from our We used k-fold cross-validation to assess the relative predic- models varied between seasons for many of the species tive ability of each of the occupancy models (Boyce et al., (Table 1). Caracals and honey badgers, for example, had stron- 2002; Hooten & Hobbs, 2015). We divided the data into five ger associations with mopane cover in the wet season. Among randomly chosen subsets; subsets (i.e. data from the same cam- the covariates, mopane cover influenced (i.e. 95% CI did not era stations) were kept consistent for all species- and season- overlap zero) the greatest number of species’ distributions, specific analyses. We fit the occupancy model 5-times, each where the majority of species were less likely to occupy time leaving out one of our data subsets and calculating total mopane dominated (Table 1). In contrast, occupancy deviance for our set-aside data using our parameter estimates probabilities of most species were not influenced by relative from the leave-in data. The model (i.e. null, resource availabil- prey availability (Table 1). Contrary to expectations, our intra- ity, intraguild species, or combined) with the lowest summed guild species model revealed that carnivores were generally deviance for all 5 subsets was considered to have the best positively associated with intraguild species, particularly com- model fit. petitors (Table 1). We found that species differed in which occurrence model Results best predicted the respective carnivore distribution (Table 2). During the dry season, our combined model best predicted the We photographed the 10 carnivore species 1935 times during distributions of lions and spotted hyenas. When comparing just our 3384 trap days in the 2014 dry season and 1768 times the resource availability and intraguild species models, how- during our 3949 trap days in the 2015 wet season (Fig. 2). We ever, we observed a consistent pattern across large carnivore also had 10 974 and 10 234 detections of prey species during species where resource availability better predicted species’ the dry and wet season, respectively. With the exception of distributions than the detection rate of intraguild competitors , carnivores were photographed more often in the dry and predators (Table 2). In contrast, during the wet season, the season than in the wet, or photographed at similar rates intraguild species model best predicted distributions of leop- between seasons (Fig. 2). Spotted hyenas were photographed ards, spotted hyenas, and African wild dogs, where species most often and servals least (Fig. 2). We found spotted hyenas were positively associated with the detection rates of intraguild were the most widely distributed carnivores during both competitors and predators (Table 2). Similarly, the relative

Dry season Wet season 1 266 459 92 205 468 74 0.9 191 283 82 217 0.8 120 265 66 0.7 162 237 94 0.6 145 46 81 0.5 150 0.4 0.3 0.2 Probability of occupancy 0.1 0 Serval Wildcat Caracal Leopard Wild dog Black-backed jackal

Figure 2 Mean estimated probabilities of occupancy, corresponding 95% credible intervals, and raw numbers of independent photo detections for meso- and large carnivores during camera trap surveys in Ngamiland District, Botswana during the 2014 dry season and 2015 wet season.

Journal of Zoology (2017) – ª 2017 The Zoological Society of London 5 Carnivore distributions in Botswana L. N. Rich et al.

Table 1 Covariate effects and the corresponding standard errors on carnivore species’ probabilities of occupancy during the dry season of 2014 (August–November) and wet season of 2015 (February–April) in Ngamiland District, Botswana

Resource availability model Intraguild species model Species Mopane EVI Water Prey Competitors Predators Dry season Large carnivores Leopard 0.7 (0.72) 0.3 (0.53) 0.3 (0.33) 1.1 (1.15) 1.2 (1.01) 0.5 (0.48) Lion 2.2 (0.91)a 1.1 (0.67)a 0.9 (0.54) 0.5 (0.62) 0.4 (0.27) Spotted hyena 0.9 (0.82) 0.2 (0.96) 0.5 (0.59) 1.4 (1.21) 0.9 (0.74) Wild dog 1.3 (1.32) 0.8 (1.16) 0.6 (0.99) 1.2 (2.79) 1.1 (1.01) 0.4 (1.20) Meso-carnivores BB jackalb 1.0 (0.43)a 2.1 (0.73)a 0.5 (0.37) 0.0 (0.36) 1.0 (0.35)a 0.1 (0.24) Caracal 0.4 (0.32) 0.1 (0.34) 0.2 (0.35) 0.1 (0.27) 0.1 (0.35) 1.4 (0.77)a Civet 1.3 (0.33)a 0.1 (0.29) 0.3 (0.27) 0.0 (0.25) 0.7 (0.41)a 0.6 (0.44) Honey badger 0.2 (0.58) 1.0 (0.56)a 0.7 (0.48) 2.2 (1.13)a 0.5 (0.39) 0.1 (0.37) Serval 2.2 (0.61)a 0.4 (0.39) 0.8 (0.48) 0.6 (0.34) 1.3 (0.53)a 0.6 (0.56) Wildcat 0.3 (0.40) 1.9 (0.64)a 2.3 (1.07)a 0.1 (0.35) 1.5 (0.79)a 0.0 (0.29) Wet season Large carnivores Leopard 1.0 (0.88) 0.5 (0.84) 0.1 (0.81) 2.3 (2.32) 1.5 (1.65) 3.3 (1.47)a Lion 2.3 (0.89)a 0.8 (0.79) 0.3 (0.66) 2.3 (1.62)a 1.2 (1.21) Spotted hyena 0.0 (0.37) 0.1 (0.34) 0.2 (0.38) 1.8 (0.84)a 2.2 (0.86)a Wild dog 1.3 (1.20) 0.8 (0.82) 1.5 (1.11) 1.7 (1.50) 0.2 (0.69) 1.3 (0.62)a Meso-carnivores BB jackalb 0.6 (0.28)a 1.0 (0.28)a 1.1 (0.35)a 0.4 (0.28) 0.4 (0.23)a 0.1 (0.20) Caracal 0.9 (0.89)a 0.5 (0.31) 0.1 (0.52) 0.1 (0.49) 0.2 (0.43) 0.3 (0.29) Civet 1.7 (0.35)a 0.5 (0.29) 0.5 (0.30) 0.1 (0.27) 1.5 (0.45)a 0.1 (0.23) Honey badger 0.7 (0.25)a 0.1 (0.21) 0.1 (0.23) 0.0 (0.22) 1.5 (0.45)a 0.1 (0.23) Serval 5.2 (4.63)a 0.6 (1.20) 0.8 (1.41) 4.5 (7.48) 3.9 (1.61)a 0.2 (0.56) Wildcat 0.3 (0.36) 0.3 (0.31) 0.2 (0.33) 0.8 (0.64) 1.5 (0.73)a 0.3 (0.31) a95% credible interval did not overlap 0.0. bBB jackal = black-backed jackal.

Table 2 Comparison of deviance statistics (D deviance, model with lowest deviance value has best model fit) for models used to predict carnivore occupancy (w) probabilities during the 2014 dry season and 2015 wet season in Ngamiland District, Botswana. We compared null (no w covariates), resource availability (w covariates = mopane, prey, water, EVI), intraguild species (w covariates = detection rate of intraguild competitors and predators), and combined (w covariates = all covariates) models

Large carnivores Meso-carnivores Spotted Wild Black-backed African Honey Leopard Lion hyena dog jackal Caracal civet badger Serval Wildcat 2014 Null 6.03 35.89 4.22 0.00 2.13 0.56 8.82 1.45 8.55 1.35 Dry Season Resource availability 0.00 15.49 0.34 1.06 2.41 5.54 0.00 9.93 1.73 3.39 Intraguild species 4.42 30.25 3.02 5.05 0.00 0.00 7.66 0.00 0.00 1.35 Combined 0.38 0.00 0.00 4.99 8.45 6.58 12.80 29.69 5.17 0.00 2015 Null 1.91 7.22 5.85 1.89 21.61 1.51 40.93 13.69 80.95 5.85 Wet Season Resource availability 0.69 0.32 4.36 3.90 2.57 1.37 7.13 7.77 34.20 10.51 Intraguild species 0.00 6.67 0.00 0.00 13.85 3.19 25.91 53.61 0.00 0.00 Combined 0.25 0.00 0.24 0.22 0.00 0.00 0.00 0.00 25.56 8.56

influence of intraguild species and/or resource availability on and elsewhere in Africa, many members of the diverse carni- the distributions of meso-carnivores varied between seasons. vore guild compete for similar prey species (Estes, 1991; Caro The intraguild species model best predicted the distributions of & Stoner, 2003) resulting in a strong dominance hierarchy the majority of in the dry season, while the where larger members of the guild have been observed killing combined model was generally the best predictive model in or harassing smaller members and stealing their kills (Caro & the wet season (Table 2). Stoner, 2003). Theory suggests this will result in top predators matching the distributions of their resources while the subordi- Discussion nate predators balance the trade-off of resource acquisition and risk (Palomares & Caro, 1999; Heithaus, 2001). Temporal par- Our research examined the influence of both intraguild species titioning has been suggested as a mechanism for coexistence activity and resource availability on the seasonal distributions within the carnivore guild yet in our study area, large carni- of 10 carnivore species in northern Botswana. In Botswana, vores have extensive temporal overlap (Cozzi et al. 2012).

6 Journal of Zoology (2017) – ª 2017 The Zoological Society of London L. N. Rich et al. Carnivore distributions in Botswana

Overall, our research found the majority of seasonal carnivore covariate relationship would not be impacted. Further work distributions were best predicted by either the activity rate of focused on both on- and off-road sites would help elucidate intraguild species or a combination of both resource availabil- the extent to which observations are dependent on this land- ity and intraguild species. Contrary to theory, we found carni- scape feature. vores were generally positively associated with the activity rate Our estimates of prey availability were based on prey detec- of intraguild species and that the wet season distributions of tion rates from the camera trap surveys and were found to three top predators (i.e. leopards, spotted hyenas and African influence the distributions’ of a small number of species when wild dogs) were best predicted by the activity rate (i.e. there compared to photographic trapping rates of intraguild competi- was a positive association) of intraguild competitors and preda- tors and predators. This suggests detections of intraguild spe- tors. cies did a better job representing the distributions of resources The generally positive associations we found among carni- (e.g. prey) on the landscape than our estimated measure of vore species differ from areas such as the Serengeti, where prey availability as it is unlikely species actively sought areas subdominant carnivores can be limited by, and often locally occupied by other carnivores, with the possible exception of excluded from, prey-rich areas by competition with dominant carrion feeding species (e.g. black-backed jackals and spotted carnivores (Creel & Creel, 1996; Durant, 1998). Previous stud- hyenas). Our measure of prey availability did not account for ies have found that the negative effects of intraguild the vulnerability of prey to predation or include small mam- are reduced in structured habitats (Janssen et al., 2007). For mals and , which are a critical part of many meso-carni- example, spotted hyenas are more likely to detect and steal vore diets (Estes, 1991). We recommend future studies better wild dog kills in open areas where visibility is good than in account for prey availability potentially through independent thickly vegetated areas (Gorman et al., 1998). Thus, the diver- field methods such as distance sampling and small gent evidence of competitor/predator avoidance in the two sys- trapping, to improve model inferences. tems may be attributed to extreme differences in habitat (i.e. In general, we found seasonal occupancy patterns of most the densely vegetated woodland savanna of the Okavango carnivore species were influenced by a unique combination of Delta versus the short grass plains of the Serengeti). The influ- resource availability and detection rates of intraguild species. ence of intraguild competitors/predators has also been found to Among the resources included, mopane cover had the largest vary depending on species’ densities, with reduced rates of influence on species’ distributions where species were gener- when species occur at low densities (Holt ally less likely to occupy areas dominated by mopane shrub & Polis, 1997; Heithaus, 2001; Caro & Stoner, 2003). There- and woodlands. This is consistent with previous studies report- fore, we also suggest that differences between the two systems ing greater of African in may be attributed to carnivore densities. Carnivore densities in savannas (Oindo & Skidmore, 2002; Rich et al., 2016). In our study area may be below the threshold at which competi- contrast with previous studies (Pettorelli et al., 2010; Schuette tor/predator avoidance begins shaping community structure et al., 2013), we found no evidence that permanent water was (Holt & Polis, 1997; Palomares & Caro, 1999; Caro & Stoner, a significant predictor of carnivore distributions. This is likely 2003). Lastly, the strength and frequency of intraguild interac- because we were unable to account for smaller pans and sea- tions varies based on resource availability (Rosenzweig, 1966; sonal, ephemeral water sources. Lastly, we found vegetation Palomares & Caro, 1999; Heithaus, 2001; Caro & Stoner, productivity, as measured by EVI, was negatively related to 2003). If prey resources were relatively plentiful in our study lion, honey badger, and wildcat occupancy in the dry season area, the influences of intraguild competition and predation and black-backed jackal occupancy in both seasons. The nega- may have been minimized. tive association is likely because at high levels of productivity, Inferring ecological interactions from our observed patterns there is increased production of woody species which can of occupancy is challenging as other factors such as the place- result in reduced plant diversity and of ment of camera traps or our measure of prey availability could grass (Oindo & Skidmore, 2002). This, in turn, may result in influence observed patterns (MacKenzie et al., 2005). We sam- lower occupancy of the many prey species that select for open pled on sand roads to maximize detection probabilities of car- grasslands and floodplains (Rich et al., 2016). nivores (Forman & Alexander, 1998). Our sampling design The composition of ecological communities is shaped by the may have affected results if the relationships we observed interplay between competitive interactions and species’ habitat depended on cameras being on roads. For conclusions to be and food requirements (Rosenzweig, 1966; Holt, 1984). Sup- affected, the relationship between occupancy and a covariate porting this, our research found that nearly half of the seasonal would have to differ between on-road and off-road areas. carnivore distributions were best predicted by models that While cognizant of this possibility, we believe the general rela- accounted for both resource availability and the activity of tionships we observed are applicable to the area as a whole intraguild species. The generally positive associations that we because: 1) our sampling locations on low-use, 4-wheel drive found among intraguild species suggests competitor/predator roads represented a random sample of the landscape, 2) we avoidance did not play a large role in shaping carnivore com- had high detection rates for all endemic carnivores and prey munity structure in northern Botswana. As natural environ- species, and 3) even if our prey availability or intraguild spe- ments continually change, studies similar to ours are pertinent cies covariates were positively or negatively biased, these as they can be used to monitor distributions of wildlife com- biases were likely consistent across the study area (i.e. a spe- munities and elucidate the combined influences of resource cies either used or avoided roads) meaning the occupancy- availability and intraguild species activity. A comprehensive

Journal of Zoology (2017) – ª 2017 The Zoological Society of London 7 Carnivore distributions in Botswana L. N. Rich et al. knowledge of how wildlife responds to dynamic Lautenbach, S. (2013). Collinearity: a review of methods to will enable managers to better predict how system changes deal with it and a simulation study evaluating their may impact carnivore communities, leading to proactive performance. Ecography 36,27–46. instead of reactive management decisions and better informed Durant, S.M. (1998). Competition refuges and coexistence: an conservation planning (Pettorelli et al., 2010). example from Serengeti carnivores. J. Anim. Ecol. 67, 370– 386. Acknowledgements Estes, R.D. (1991). The behavior guide to African mammals: including hoofed mammals, carnivores, and . We thank the government of Botswana, the Ministry of the Berkeley: The University of California Press. Environment, Wildlife and Tourism and the Department of Forman, R.T.T. & Alexander, L.E. (1998). 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8 Journal of Zoology (2017) – ª 2017 The Zoological Society of London L. N. Rich et al. Carnivore distributions in Botswana

Ripple, W.J., Estes, J.A., Beschta, R.L., Wilmers, C.C., Ritchie, E.G., Hebblewhite, M., Berger, J., Elmhagen, B., Letnic, M., Supporting Information Nelson, M.P., Schmitz, O.J., Smith, D.W., Wallach, A.D. & Additional Supporting Information may be found in the online Wirsing, A.J. (2014). Status and ecological effects of the version of this article: world’s largest carnivores. Science 343, 151–162. Rosenzweig, M.L. (1966). Community structure in sympatric Appendix S1. Prey species and their corresponding weight fi carnivora. J. Mammal. 47, 602–612. classi cations, number of indepdent detections (# det.) during Schuette, P., Wagner, A.P., Wagner, M.E. & Creel, S. (2013). the 2014 dry season, and number of detections during the Occupancy patterns and niche partitioning within a diverse 2015 wet season. Appendix S2. carnivore community exposed to anthropogenic pressures. Carnivore species and their corresponding com- petitors and predators. Biol. Conserv. 158, 301–312. Woodroffe, R. & Ginsberg, J.R. (1998). and the extinction of populations inside protected areas. Science 280, 2126–2128.

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