Application of maximum entropy (MaxEnt) to understand the spatial dimension of human–wildlife conflict (HWC) risk in areas adjacent to Gonarezhou National Park of Zimbabwe

Abstract

The application of empirical and spatially explicit information to understand the spatial distribution of human–wildlife conflict (HWC) risk zones is increasingly becoming imperative to guide conservation planning and device mechanisms to enhance and sustain the coexistence between wildlife and humans. Spatial information on HWC is scarce in the literature, and previous studies have tended to concentrate more on the human dimensions of HWC. Although normally applied in wildlife studies, species distribution modeling (SDM) is becoming an indispensable tool to predict and visualize potential risk zones for HWC. In this study, we used maximum entropy (MaxEnt), a presence-only SDM to predict the potential distribution of HWC risk zones and to determine ecological variables that significantly explain the spatial distribution of HWC occurrences around the Gonarezhou National Park (GNP) in southeastern Zimbabwe. Our results show that HWC risk zones are not randomly distributed but tend to be concentrated along areas adjacent to protected areas that support potential overlaps and contacts between wildlife and human landscapes. A distinctive HWC high-risk zone is observed north of GNP, around areas such as Chitsa, Mpinga, and Masekesa—communities that should be prioritized for proactive mitigation interventions. In view of limited conservation resources typical of less developed countries, wildlife managers are pressed to explicitly determine zones with the highest HWC risks for effective and targeted interventions. Findings from this study thus provide a crucial baseline for identifying potentially high-risk HWC zones and the main predictors, knowledge that can be streamlined for proactive resource allocation to mitigate the HWC challenge.

Keywords

• gonarezhou national park
• human–livestock–wildlife interface
• human–wildlife conflict
• maximum entropy
• zimbabwe