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

Abstract

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