Drivers of leopard (Panthera pardus) habitat use and relative abundance in Africa's largest transfrontier conservation area

Abstract

Transfrontier conservation areas (TFCAs) have the potential to provide havens for large carnivores while preserving connectivity across wider mixed-use landscapes. However, information on the status of species in such landscapes is lacking, despite being a prerequisite for effective conservation planning. We contribute information to this gap for leopard (Panthera pardus) in Africa, where the species is facing severe range contractions, using data from transect surveys of a 30,000km 2 area across Botswana and Zimbabwe in the Kavango-Zambezi (KAZA) TFCA. We used occupancy models to assess how biotic, anthropogenic, and management variables influence leopard habitat use, and Nmixture models to identify variables influencing the species' relative abundance. Leopard were detected in 184 out of 413 sampling units of 64km 2 ; accounting for imperfect detection resulted in mean detection probability ̂̅ = 0.24 (SD = 0.06) and mean probability of site use ψ ̅ = 0.89 (SD = 0.20). Habitat use was positively influenced by prey availability and high protection. Relative abundance was best predicted by trophy hunting, which had a negative influence, while abundance was positively associated with high protection and availability of steenbok. Our findings suggest that securing prey populations should be a priority in conservation planning for leopard in Africa, and underline the necessity of preserving highlyprotected areas within mixed-use landscapes as strongholds for large carnivores. Our findings also support calls for better assessment of leopard population density in trophy hunting areas, and illustrate the value of N-mixture models to identify factors influencing relative abundance of large carnivores.

Prey availability and intraguild competition regulate the spatiotemporal dynamics of a modified large carnivore guild

“…Our findings show that prey detection is a significant predictor of detection for both hyena and leopard, supporting our predictions and in accordance with previous studies (Höner et al., 2005; Périquet et al., 2015; Ramesh et al., 2017; Searle et al., 2020). Leopard detection was also explained by proximity to water, as observed in previous studies (Balme et al., 2007; Havmøller et al., 2019).…”

“…Spatiotemporal dynamics between leopard and hyena are complex, with findings varying between habitats and carnivore community assemblages. The availability of preferred prey is known to significantly influence the presence of both species (Balme et al., 2019; Périquet et al., 2015; Searle et al., 2020). In addition, leopard kills are subject to high levels of kleptoparasitism from hyena (Balme et al., 2017), which is known to affect reproductive success in female leopards (Balme et al., 2013).…”

“…We applied co‐detection modeling (Balme et al., 2019; Cusack et al., 2017), time‐to‐event analyses (Cusack et al., 2017), and temporal overlap comparisons (Rowcliffe et al., 2014) to evaluate the impact of a range of interspecific, ecological, and anthropogenic parameters on carnivore activity. The availability of preferred prey has previously been highlighted as a significant driver of leopard and hyena presence (Périquet et al., 2015; Searle et al., 2020), and accordingly, we predict that (a) the detection of both species will increase in relation to prey detectability, (b) this will result in significant rates of co‐detection between leopard and hyena, and (c) the potential for high levels of spatial overlap between leopard and hyena will result in leopard adopting temporal avoidance mechanisms to facilitate coexistence and avoid competition.…”

Random forest modelling of multi‐scale, multi‐species habitat associations within KAZA transfrontier conservation area using spoor data

“…Kaszta et al, 2016). In turn, many African carnivore species are known to make habitat use decisions based on prey availability (Everatt et al, 2015; Searle et al, 2020). As we did not explicitly include information on prey in our carnivore models, it is likely that climatic, biogeochemical, and vegetation variables ranked highly in our carnivore models as they acted as proxies for prey.…”

Turning ghosts into dragons: improving camera monitoring outcomes for a cryptic low-density Komodo dragon population in eastern Indonesia

“…The key requirement to establish effective conservation actions for apex predators is to accurately monitor population trends and status (Karanth et al 2011). However, because apex predators are often rare or averse to capture or detect, non-invasive monitoring methods are routinely used to evaluate the effects of threatening processes or individual-based recapture or resighting probabilities can be too low to allow for the alternate population estimates using markrecapture type models (Williams et al 2002;Ke ´ry and Schmidt 2008;Couturier et al 2013;du Preez et al 2014;Tan et al 2017;Searle et al 2020).…”