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<p>Human-wildlife conflict (HWC) implied in this study as direct negative interactions between people and wildlife livelihoods loss, property damage and threat to safety remains a global challenge for conservation and sustainable development. However, the modelling approaches used to predict where and when HWC occurs remain limitedly explained. We conducted a systematic review (2008–2024) following the Preferred Reporting Items for Systematic Reviews 2020 guidelines to examine taxonomic focus and modeling methods in HWC research. Searches across four databases identified 81 records of which 41 met strict inclusion criteria that required for a peer review, thesis or United Nations report and modelling. We extracted data on country of study, year of publication, taxonomic focus and modeling approach then analysed these descriptively. Number of publications increased after 2016 with highest in 2023 (n = 10). Mammals dominated the literature (95%) while reptiles and birds were rarely studied indicating a taxonomic bias. Generalised Linear Models (GLM) and Poison Regression were common but advanced machine learning techniques were lowly adopted. To reduce bias, duplicate screening and independent screening were applied. The review reaffirms a reliance on conventional models and large mammal systems which limit inferences to other taxa. As such, we recommend broader taxonomic inclusion and adoption of machine –learning approaches to improve predictive capacity and policy relevance in HWC research.</p>

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A systematic review of modelling approaches and taxonomic focus for studying human wildlife conflict patterns

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月2日

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  • Petros Kachulu,
  • Harineck Mayamiko Tholo,
  • Tiwonge I. Mzumara,
  • Chikondi Chisenga,
  • Steven Gondwe,
  • Jabulani Nyengere

摘要

Human-wildlife conflict (HWC) implied in this study as direct negative interactions between people and wildlife livelihoods loss, property damage and threat to safety remains a global challenge for conservation and sustainable development. However, the modelling approaches used to predict where and when HWC occurs remain limitedly explained. We conducted a systematic review (2008–2024) following the Preferred Reporting Items for Systematic Reviews 2020 guidelines to examine taxonomic focus and modeling methods in HWC research. Searches across four databases identified 81 records of which 41 met strict inclusion criteria that required for a peer review, thesis or United Nations report and modelling. We extracted data on country of study, year of publication, taxonomic focus and modeling approach then analysed these descriptively. Number of publications increased after 2016 with highest in 2023 (n = 10). Mammals dominated the literature (95%) while reptiles and birds were rarely studied indicating a taxonomic bias. Generalised Linear Models (GLM) and Poison Regression were common but advanced machine learning techniques were lowly adopted. To reduce bias, duplicate screening and independent screening were applied. The review reaffirms a reliance on conventional models and large mammal systems which limit inferences to other taxa. As such, we recommend broader taxonomic inclusion and adoption of machine –learning approaches to improve predictive capacity and policy relevance in HWC research.