Habitat Preferences, Movement Patterns and Spatial Clustering of African Savannah Elephants ( <i>Loxodonta africana</i> ) in Mana Pools National Park ( <scp>MPNP</scp> ), Zimbabwe

ABSTRACT Understanding elephant habitat use and movement is essential for conservation in dynamic, resource‐limited semi‐arid ecosystems. Remote sensing and GPS telemetry provide powerful tools for quantifying elephant ecological patterns across heterogeneous landscapes. This study investigates the habitat preferences, movement patterns and spatial clustering of African savannah elephants ( Loxodonta africana ) in Mana Pools National Park (MPNP), Zimbabwe, using GPS telemetry data and remote sensing–derived environmental variables. Habitat suitability was modelled using the maximum entropy (MaxEnt) modelling technique. Seasonal home ranges and movement dynamics were analysed using minimum convex polygons (MCP), Voronoi polygons and the Time local convex hull (T LoCoH) techniques. Clusters were identified using the K‐means method. The MaxEnt results revealed that proximity to permanent water sources particularly, the Zambezi River was the most significant predictor of elephant distribution, contributing over 96% to the model's performance. Home range size estimates varied across methods and seasons. During the dry season, MCP, Voronoi polygons and time local convex hull (T‐LoCoH) estimated ranges of 406 km 2 , 521 km 2 and 325 km 2 , respectively. In the wet season, home ranges expanded markedly to 975 km 2 (MCP), 713 km 2 (Voronoi) and 527 km 2 (T‐LoCoH). The transition season recorded the largest ranges, with 1065 km 2 (MCP), 1032 km 2 (Voronoi) and 714 km 2 (T‐LoCoH). MCP consistently produced the largest estimates, while T‐LoCoH yielded the smallest, highlighting methodological sensitivity in quantifying elephant ranging behaviour across seasonal landscapes. K‐means clustering identified spatially distinct movement clusters across all seasons, showing non‐random habitat use driven by environmental constraints such as water availability, vegetation productivity and terrain. Findings support data‐driven conservation strategies for sustainable elephant management in protected areas.