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Innovative Geographic Information Science (GIS) and Remote Sensing Tools for Modelling the Ranging Behaviour and Habitat Dynamics of the African Savannah Elephant (Loxodonta africana) in Mesic Protected Areas

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dc.contributor.author Mukomberanwa, Nobert Tafadzwa
dc.contributor.author Taru, Phillip
dc.contributor.author Utete, Beaven
dc.contributor.author Ngorima, Patmore
dc.date.accessioned 2026-05-12T07:57:31Z
dc.date.available 2026-05-12T07:57:31Z
dc.date.issued 2024-12-02
dc.identifier.citation Mukomberanwa, N. T., Taru, P., Utete, B., & Ngorima, P. (2024). Innovative geographic information science (GIS) and remote sensing tools for modelling the ranging behaviour and habitat dynamics of the African savannah elephant (Loxodonta africana) in mesic protected areas. African Journal of Ecology, 62(4), e70000. en_US
dc.identifier.issn https://doi.org/10.1111/aje.70000
dc.identifier.uri https://ir.cut.ac.zw:8080/xmlui/handle/123456789/752
dc.description.abstract Transboundary wildlife species like the African savannah elephant (Loxodonta africana) requires a comprehensive regional approach to monitoring and effective conservation. This requires a thorough understanding of their ecology, ranging behaviour and the distribution of suitable habitats. In diverse landscapes, the management and conservation of the African savannah elephant are critical, particularly in dry protected areas where water and food resources are limited. The use of innovative Geographic Information Science (GIS) and remote sensing tools is revolutionising the understanding of the ranging behaviour and habitat dynamics of the African savannah elephant. When adopting GIS and remote sensing tools, park managers and conservationists must remember that: (i) the African savannah elephant has a determinate movement pattern and clusters around dominant vegetation types, (ii) the soil-adjusted vegetation index (SAVI) performs better relative to other indices in modelling the distribution of the African savannah elephant in arid areas, (iii) cellular automata–artificial neural network (CA-ANN) is a robust technique in modelling future landscapes, (iv) landscapes or environments near water points are significantly utilised by the African savannah elephant and vegetation performance is usually better far from the piosphere, (v) significant difference in the size of the home ranges and habitat selection by the African savannah elephant is mostly influenced by vegetation type and seasonal variations of resources, (vi) hyperslender stems in forest gaps confirms minimal damage in African savannah elephant dominated landscapes (satellite data confirms evidence of high tree regeneration) and (vii) the dynamic Brownian Bridge Movement Model (dBBMM) is a smart technique for home range and utilisation distribution construction in different protected zones. en_US
dc.language.iso en en_US
dc.publisher Willey en_US
dc.subject African savannah elephant en_US
dc.subject GIS en_US
dc.subject habitat selection en_US
dc.subject remote sensing en_US
dc.subject Savannah ecosystems en_US
dc.subject transboundary en_US
dc.title Innovative Geographic Information Science (GIS) and Remote Sensing Tools for Modelling the Ranging Behaviour and Habitat Dynamics of the African Savannah Elephant (Loxodonta africana) in Mesic Protected Areas en_US
dc.type Article en_US
dc.identifier.orcid 0009-0003-1896-9813 en_US
dc.identifier.orcid 0009-0002-5158-6032 en_US
dc.identifier.orcid 0000-0001-5493-4421 en_US
dc.identifier.orcid 0000-0002-3135-8270 en_US


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