Knowledge KudakwasheMawere | Wildlife Conservation | Best Researcher Award

Mr. Knowledge Kudakwashe Mawere | Wildlife Conservation | Best Researcher Award

Mr. Knowledge Kudakwashe Mawere, Zimbabwe Parks and Wildlife Management Authority, Zimbabwe

Knowledge Kudakwashe Mawere is a GIS and Earth Observation expert serving as a Terrestrial Ecologist at Zimbabwe Parks and Wildlife Management Authority. With over three years of conservation experience, he specializes in GIS, remote sensing, terrestrial ecology, and database management. His work includes ecological feasibility studies, spatial modeling, and SMART database training. An award-winning graduate with a BSc in GIS and Earth Observation (First Class) from the University of Zimbabwe, Knowledge has published research on wildlife conservation and human-wildlife conflict. Passionate about biodiversity and community welfare, he is also skilled in programming, data analytics, and mentoring students. 🐾🌿

Publication Profile

Work Experience

Knowledge Kudakwashe Mawere is a dedicated Terrestrial Ecologist at the Zimbabwe Parks and Wildlife Management Authority (2024–present) based in Hwange National Park. He excels in ecological feasibility assessments, research project formulation, and fieldwork coordination. As a GIS and remote sensing expert, he contributes to species management plans, data analysis, and training programs. Previously, he interned as an Ecologist (2022–2024) and served as a GIS Guest Lecturer (2021–2022), mentoring students and conducting research. Additionally, he has been a SMART database trainer since 2020, preparing training materials and leading conservation-focused workshops. His work reflects a passion for wildlife and sustainable ecosystems. 🐾🌿

 

🎓 Academic Achievements

Knowledge Kudakwashe Mawere holds a Bachelor of Science Honours in Geographical Information Science and Earth Observation (First Class) from the University of Zimbabwe (2018–2022). His research focused on spatial modeling of large ungulate biomass across Zimbabwe’s protected areas, showcasing his expertise in wildlife conservation. In 2023, he further enhanced his skills by earning a Certificate in Data Science Foundations from the Great Learning Academy in India. These qualifications reflect his strong foundation in GIS, earth observation, and data science, aligning with his passion for leveraging technology to advance ecological research. 🌍📊

 

🏆 Honors and Recognition

Knowledge Kudakwashe Mawere was honored with the prestigious University Book Prize in 2022 by the University of Zimbabwe, Mount Pleasant, Harare. This award reflects his academic excellence and dedication to his studies in Geographical Information Science and Earth Observation. It highlights his commitment to achieving top-tier performance in his field, further solidifying his reputation as a promising researcher and scholar in ecological conservation and GIS applications. 📖🌟

 

🦓 Research Focus

Knowledge Kudakwashe Mawere specializes in ecological conservation with a focus on GIS and remote sensing applications. His research delves into spatial modeling of large ungulate biomass in protected areas, exploring habitat dynamics and wildlife management 🗺️📊. He also investigates human–wildlife conflict (HWC) risk using advanced tools like MaxEnt, emphasizing coexistence strategies near conservation zones 🐘🤝. Mawere’s work bridges ecological insights with cutting-edge technology to support sustainable conservation and biodiversity protection, contributing significantly to landscape ecology, wildlife conservation, and resource management in Zimbabwe. 🌿🌍

 

Publication Top Notes 📚

  • 🗓️ 2024: Spatial modelling of large ungulate biomass in the gazetted protected areas and conservancies in Zimbabwe – Geology, Ecology, and Landscapes. DOI: [10.1080/24749508.2024.2430047] (Cited: Not yet available). 🦓🌍
  • 🗓️ 2023: 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 – Ecology and Society. DOI: [10.5751/es-14420-280318] (Cited: Not yet available). 🐘📊