Predictive modelling of leopard predation using contextual Global Positioning System cluster analysis

Abstract

Apex predators are essential for the viability of healthy ecosystems. By studying carnivoran feeding ecology, we can obtain a better understanding of the ecological limits, resilience and predator–prey dynamics that govern these populations. However, monitoring elusive predators – like the leopard Panthera pardus – is often fraught with logistical and financial constraints, particularly in inaccessible terrain.

In this study, we identified clusters of Global Positioning System (GPS) points from four GPS-collared leopards and investigated them in the field for potential kills. Environmental data from cluster sites were gathered alongside spatial and temporal data collected via GPS cluster analysis to develop statistical models capable of predicting the occurrence of leopard predatory events.

Our results demonstrate that leopard predation can be accurately modelled either by using a combination of field data (i.e. collected at cluster sites) and remote data (i.e. obtained via GPS analysis), or simply remote data alone. Kills were more likely to be present at clusters where leopards exhibited longer handling times, at sites with dense vegetation cover, when leopards were more active 12 h before the cluster than 12 h after, where more tree refugia were present, in areas of higher elevation, at sites containing low levels of shrub cover, and when clusters began during diurnal or crepuscular hours.

By using this modelling approach, comprehensive predation studies are not only efficient and cost-effective but also logistically feasible across a range of different habitats and may even be applied to other carnivorans. It provides a detailed analytical means of studying broader aspects of carnivoran feeding ecology, such as predation habits, carrying capacity, ecological hunting requirements and species interactions, which are important aspects of carnivoran management.