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

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.

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Publication Details

Journal
African Journal of Ecology
Published
2024-12-01
DOI
https://doi.org/10.1111/aje.70000
Citations
1
Primary Topic
Wildlife Ecology and Conservation
Type
article
Field-Weighted Citation Impact
0.46
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article

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

Phillip Taru, Patmore Ngorima, Beaven Utete, Nobert Tafadzwa Mukomberanwa
1 citations
African Journal of Ecology
Wildlife Ecology and Conservation
0.46
article

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

Phillip Taru, Patmore Ngorima, Beaven Utete, Nobert Tafadzwa Mukomberanwa
article en
1 citations

Abstract

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.

African Journal of EcologyVol. 62(4)
Chinhoyi University of Technology (ZW)
Openalex Percentile: Top 27%
Wildlife Ecology and Conservation
0.46
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