Kavango–Zambezi Transfrontier Conservation Area 795


for crop cultivation. The climate is hot and semi-arid, with a mean annual temperature of 22–24 °C and annual rainfall of 350–400 mm, concentrated in the wet season during November–March (Atlas of Namibia Team, 2022).


Methods


Data collection During July–September 2022 we deployed unbaited camera traps (Browning Strike Force Pro XD, Browning, USA) at 35 locations facing unpaved roads (Fig. 1). We overlaid an 8 × 8 km grid (2,304 km2) over the study area, capturing a gradient of increasing human activity from west to east. We targeted camera-trap placement near the centroid of each grid cell to ensure independence at sampling locations and to minimize spatial autocorrelation. This was not al- ways possible because of the inaccessibility of the area, re- sulting in four unsampled cells and four cells with two camera-trap locations. Two additional cells were unsampled because of missing camera traps, reducing the total number of camera-trap locations to 33. Mean camera trap spacing was 6.1 km and met the assumptions of spatial independ- ence for most species (Rich et al., 2019). To increase detec- tion, we placed two camera traps per location, facing each other at a slight angle and mounted c. 75 cm above the ground and 5 m apart. We programmed the camera traps to take pictures with a 5 min delay between triggers, to save battery life. Each trigger produced a burst of five images taken within 1 s. The camera traps were active for 2 months. Weclassified animals in images to species using TrapTagger, an open-source web application that uses artificial intelli- gence (AI) in combination with amanual annotation inter- face to process camera-trap data (WildEye, 2023). We relied on MegaDetector (part of TrapTagger) for the detection of animals and the removal of empty images. We manually annotated all detections using the TrapTagger interface and subsequently verified these with AI-generated annota- tions based on a southern African species classifier. Camera- trap detections were considered independent if they were separated by at least a 30 min interval (O’Brien et al., 2003).


Data analysis


We used rarefaction analysis to evaluate species richness from the results of sampling. We created the rarefaction curve by randomly resampling the pool of survey locations and plotting the cumulative number of species records at each additional location (Gotelli&Colwell, 2001).We performed this analysis for both carnivore and prey species using the package Rarefy (Thouverai et al., 2021)in R 4.2.2 (R Core Team, 2022). To examine patterns of carnivore occurrence, we analysed detection/non-detection data from camera traps in a multi-species occupancy framework using the R package


spOccupancy (Doser et al., 2022). This approach treats species-specific regression coefficients for occupancy and detection as random effects arising from community-level normal distributions. This leads to greater precision of species-specific effects, in particular for rare species. It also presents a community-level estimate for occurrence and detection and facilitates estimation of biodiversity me- trics (Dorazio & Royle, 2005; Devarajan et al., 2020). We performed a posterior predictive check of all model objects as a goodness-of-fit assessment to validate whether the gen- erated data aligned with the observed data. Reported Bayesian P-values close to 0.5 indicate adequate model fit, whereas values , 0.1 or .0.9 suggest that the model did not fit the data well (Hobbs & Hooten, 2015). We ran one model for the dry season only and consid-


ered four sampling occasions of 14 days each to meet the closure assumption. Carnivore distributions across Namibian rangelands appear to be influenced by the availability of key resources during the dry season, yet there could be contrasting covariate associations between seasons (Verschueren et al., 2021). We included three site covariates related to anthropo- genic disturbance and resource availability that could explain carnivore probability of occurrence. (1)Weincludedthe Euclidean distance to Gam as a measure of human activity in the area. Gam is east of the study area and additional, smal- ler human settlements were more common with decreasing distance to Gam.We expected carnivores to be more common farther away from Gam. (2)Weincludedthe Euclideandis- tance to the neighbouring Nyae Nyae Conservancy (also in the KAZA TCA) as a surrogate of safety for carnivores. Nyae Nyae Conservancy has a strong history of conserva- tion success, and human tolerance towards wildlife in the Conservancy is high (Lendelvo et al., 2019). Source–sink dynamics could support the carnivore guild in Ondjou Conservancy, and we expected carnivores to be more com- mon close to Nyae Nyae Conservancy, as a source. (3)We included local prey richness (i.e. number of mammalian species with body mass .1 kg recorded per camera-trap location, excluding Carnivora and elephants Loxodonta africana) as a measure of resource availability for carnivores. Although this measure may not represent the suite of prey species available for smaller carnivores, local prey richness from camera-trap data is a measure of resource availability for large carnivores and mesocarnivores and therefore potentially for small carnivores that often scavenge on kills made by larger predators (Pereira et al., 2014; Sivy et al., 2017). We expected carnivores to be more common with high local prey richness. Although the multi-species occupancy accounted for im-


perfect detection, we assumed a constant detection probabil- ity across camera-trap sites as we deployed all camera-trap stations similarly, with cameras facing open areas (unpaved road surface) and with little variation in habitat. We recog- nize the potential for unaccounted variation in detection


Oryx, 2024, 58(6), 793–801 © The Author(s), 2024. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605324000024


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