Modelling the spatial-temporal distribution of tsetse (Glossina pallidipes) as a function of topography and vegetation greenness in the Zambezi Valley of Zimbabwe

In this study, we developed a stable and temporally dynamic model for predicting tsetse (Glossina pallidipes) habitat distribution based on a remotely sensed Normalised Difference Vegetation Index (NDVI), an indicator of vegetation greenness, and topographic variables, specifically, elevation and topographic position index (TPI). We also investigated the effect of drainage networks on habitat suitability of tsetse as well as factors that may influence changes in area of suitable tsetse habitat. We used data on tsetse presence collected in North western Zimbabwe during 1998 to develop a habitat prediction model using Maxent (Training AUC = 0.751, test AU = 0.752). Results of the Maxent model showed that the probability of occurrence of tsetse decreased as TPI increased while an increase in elevation beyond 800 m resulted in a decrease in the probability of occurrence. High probabilities (>50%) of occurrence of tsetse were associated with NDVI between high 0.3 and 0.6. Based on the good predictive ability of the model, we fitted this model to environmental data of six different years, 1986, 1991, 1993, 2002, 2007 and 2008 to predict the spatial distribution of tsetse presence in those years and to quantify any trends or changes in the tsetse distribution, which may be a function of changes in suitable tsetse habitat. The results showed that the amount of suitable tsetse habitat significantly decreased (r2 0.799, p = 0.007) for the period 1986 and 2008 due to the changes in the amount of vegetation cover as measured by NDVI over time in years. Using binary logistic regression, the probability of occurrence of suitable tsetse habitat decreased with increased distance from drainage lines. Overall, results of this study suggest that temporal changes in vegetation cover captured by using NDVI can aptly capture variations in habitat suitability of tsetse over time. Thus integration of remotely sensed data and other landscape variables enhances assessment of temporal changes in habitat suitability of tsetse which is crucial in the management and control of tsetse

The Spatial Distribution of Elephants (loxodonta Africana) in Relation to the Spatial Heterogeneity of Vegetation Cover in a Southern African Agricultural Landscape

Paper presented at the Annual Conference of the Remote Sensing and Photogrammetry Society (RSPSoc) Scales and Dynamics in Observing the Environment CDROM 10-12 September 2003, The University of Nottingham, UK.,We tested whether and how the probability of African elephant (Loxodonta Africana) presence was related to spatial heterogeneity of vegetation cover and in the agricultural landscape of the Sebungwe region in northwestern Zimbabwe in the early 1980s and early 1990s. We also tested to whether and how spatial changes in probability of elephant presence were related to changes in spatial heterogeneity the Sebungwe region between the abovementioned dates. A novel perspective was used to characterise spatial heterogeneity based on intensity (i.e. maximum variance in vegetation cover) and dominant scale (i.e. patch size at which intensity is manifested) while vegetation cover was estimated from a remotely sensed normalised difference vegetation index (NDVI) based on Landsat TM satellite imagery. The results indicated that the probability of elephant presence could be predicted reliably using intensity and dominant scale of spatial heterogeneity. Both the intensity and dominant scale of spatial heterogeneity explained 80 % and 93 % of the variance of the probability of elephant presence in the early 1980s and early 1990s respectively. The changes in the intensity and dominant scale of spatial heterogeneity predicted 89 % of the variance of the change in elephant presence between the 1980s and 1990s. The results of this study imply that if elephants are to be conserved in agricultural landscapes, it is important that wildlife management strategies aimed at sustaining wildlife species in agricultural landscapes take into account the spatial heterogeneity of vegetation cover, with particular attention to the dominant scale and intensity of spatial heterogeneity. In addition, the results imply the dominant scale and intensity perspective to the characterisation of spatial heterogeneity may improve the prediction of ecological patterns in the landscape such as determining the spatial distribution of wildlife species