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.

The post 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 first appeared on Ecology & Society.

应用经验和空间明确的信息来了解人类与野生动物冲突（HWC）风险区的空间分布对于指导保护规划和设备机制以增强和维持野生动物与人类之间的共存变得越来越重要。文献中关于 HWC 的空间信息很少，之前的研究往往更多地集中在 HWC 的人类维度上。虽然物种分布模型 (SDM) 通常应用于野生动物研究，但它正在成为预测和可视化 HWC 潜在风险区域不可或缺的工具。在本研究中，我们使用最大熵（MaxEnt），

我们的结果表明，HWC 风险区不是随机分布的，而是往往集中在与保护区相邻的区域，这些保护区支持野生动物和人类景观之间潜在的重叠和接触。在 GNP 以北观察到一个独特的 HWC 高风险区，周围有 Chitsa、Mpinga 和 Masekesa 等社区，这些社区应优先采取积极的缓解干预措施。

鉴于欠发达国家典型的保护资源有限，野生动物管理者必须明确确定 HWC 风险最高的区域，以采取有效和有针对性的干预措施。因此，这项研究的结果为识别潜在高风险 HWC 区域和主要预测因素提供了重要的基线，这些知识可以简化以主动分配资源以缓解 HWC 挑战。

应用最大熵 (MaxEnt) 来了解津巴布韦戈纳雷州国家公园附近地区人类与野生动物冲突 (HWC) 风险的空间维度的帖子首次发表在《生态与社会》上。