Geospatial Modelling of the Ranging Behaviour and Habitat Dynamics of the Loxodonta Africana in Protected Semi-Arid Heterogeneous Environments, Zimbabwe

Abstract

The African savannah elephant (Loxodonta africana) faces a complex myriad of conservation challenges owing to its large size and ecological habits and anthropic factors such as poaching, habitat modification, and settlement encroachment. This restricts and constricts its habit range sizes and determines the ranging behaviour of the African savannah elephant. This aspect needs to be investigated if the iconic ‘ecosystem engineer’ is to be adequately conserved in Sub-Saharan Africa. The purpose of this research was to model the ranging behaviour and habitat dynamics of the African savannah elephant in dry protected areas with diverse vegetation by developing and applying Geographic Information Science (GIS) and remote sensing techniques. The objectives of this study were to (i) analyse African savannah elephant space use as a function of dominant vegetation types (ii) develop machine learning models to assess the distribution of African savannah elephants and predict future landscape scenarios (iii) construct home range models to analyse the seasonal space use of African savannah elephants (iv) model the impact of African savannah elephants on vegetation structure and determine their effects on the regenerative power of the range, and (v) design detailed home range models and utilization distributions of the African savannah elephant to identify the most utilised land cover types in two mesic protected areas i.e. Mana Pools National Park and Gonarezhou National Park in Zimbabwe. Standard error and proportion tests were used to analyse African savannah elephant space use. The MaxEnt model was used to predict African savannah elephant preferred habitat dynamics and cellular automata artificial neural network (CA-ANN) was used for simulating future landscape scenarios. Home ranges were constructed using the minimum convex polygon (MCP), time local convex hull (TLoCoh) and the dynamic Brownian Bridge Movement Model (dBBMM). The impact of the African savannah elephant on the vegetation structure was analysed using the Generalised Linear Model (GLM), Bayesian Piecewise Regression (BPR) and Bayesian Regression Model (BRM). Habitat connectivity was modelled using the Spatial Absorbing Markov Chain (SAMC). Results of the study showed that the African savannah elephants have a determinate movement pattern and clustered around dominant vegetation types. The Soil Adjusted Vegetation Index (SAVI) performs better relative to other indices in predicting the habitat occupancy of African savannah elephants across all habitat types. There is a significant futuristic decrease in landscape suitable to sustain large populations of African savannah elephants by the year 2083. Vegetation structure and abundance exhibit significant damage along a water-land gradient and the modelled treeallometries confirms minimal damage by African savannah elephants. Satellite data confirms evidence of habitat connectedness and high tree regeneration in a semi-arid protected area. The findings also show variations in the size of the home ranges and utilisation distributions influenced by seasons (wet, transitional and dry) in different protected areas. The thesis provides the first published record of specialised conservation efforts that address habitat preferences and consumption patterns specific to the selected park's ecosystem and geographic characteristics. Data on a large landscape view of prospective African savannah elephant vegetation dynamics and their ranging behaviour provides new information which can broaden the scope of research and inform management and policy direction such as the next Elephant Management Plan in Zimbabwe. Since African savannah elephant populations depend on large landscapes, transboundary conservation and planning is vital for their long-term survival and regional sustainability