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.
