Department of Geography and Environmental Sciance

The central role of species competition in shaping community structure in ecosystems is well appreciated amongst ecologists. However species competition is a consistently missing variable in Species Distribution Modelling (SDM). This study presents results of our attempt to incorporate species competition in SDMs. We used a suit of predictor variables including Soil Adjusted Vegetation Index (SAVI), as well as distance from roads, settlements and water, fire frequency and distance from the nearest herbivore sighting (of selected herbivores) to model individual habitat preferences of five grazer species (buffalo, warthog, waterbuck, wildebeest and zebra) with the Ensemble SDM algorithm for Gonarezhou National Park, Zimbabwe. Our results showed that distance from the nearest animal sighting (a proxy for competition among grazers) was the best predictor of the potential distribution of buffalo, wildebeest and zebra but the second best predictor for warthog and waterbuck. Our findings provide evidence to that competition is an important predictor of grazer species' potential distribution. These findings suggest that species distribution modelling that neglects species competition may be inadequate in explaining the potential distribution of species. Therefore our findings encourage the inclusion of competition in SDM as well as potentially igniting discussions that may lead to improving the predictive power of future SDM efforts.

The application of empirical and spatially explicit information to understand the spatial distribution of human-wildlife conflict (HWC) risk zones is increasingly becoming imperative to guide conservation planning and device mechanisms to enhance and sustain the coexistence between wildlife and humans. Spatial information on HWC is scarce in the literature, and previous studies have tended to concentrate more on the human dimensions of HWC. Although normally applied in wildlife studies, species distribution modeling (SDM) is becoming an indispensable tool to predict and visualize potential risk zones for HWC. In this study, we used maximum entropy (MaxEnt), a presence-only SDM to predict the potential distribution of HWC risk zones and to determine ecological variables that significantly explain the spatial distribution of HWC occurrences around the Gonarezhou National Park (GNP) in southeastern Zimbabwe. Our results show that HWC risk zones are not randomly distributed but tend to be concentrated along areas adjacent to protected areas that support potential overlaps and contacts between wildlife and human landscapes. A distinctive HWC high-risk zone is observed north of GNP, around areas such as Chitsa, Mpinga, and Masekesa-communities that should be prioritized for proactive mitigation interventions. In view of limited conservation resources typical of less developed countries, wildlife managers are pressed to explicitly determine zones with the highest HWC risks for effective and targeted interventions. Findings from this study thus provide a crucial baseline for identifying potentially high-risk HWC zones and the main predictors, knowledge that can be streamlined for proactive resource allocation to mitigate the HWC challenge.

Based on wildlife and domestic herbivore waterhole use count data collected in the South Eastern Lowveld of Zimbabwe between 2008 and 2011, we tested whether there was any significant overlap by wild and domestic herbivores in the use of waterholes located; 1) inside a wildlife protected area, 2) at the boundary of the protected and agricultural areas and 3) in the agricultural areas. We also explored whether species diversity at these three land uses vary significantly. Our results showed overlap in place and time between wild and domestic herbivores especially in the use of waterholes located at the boundary of the protected areas and at one waterhole located in the agricultural areas. Results also indicated that overall, 95% of wild herbivores prefered using waterholes located inside the protected area.Results of this study further demonstrated that animal species diversity declines significantly along a gradient from protected areas to agricultural areas. Overall, results of this study imply that the current emerging disease interface between wildlife and livestock may not necessarily be due to direct physical interaction between disease reservoir hosts or even sharing of same waterholes at the same time but rather an indirect contact. These findings could provide an important avenue of investigating livestock and wildlife disease outbreaks at the domestic-wildlife interface which are disturbing current efforts to improve livestock production as well as biodiversity conservation in African savannas.

The central role of species competition in shaping community structure in ecosystems is well appreciated amongst ecologists. However species competition is a consistently missing variable in Species Distribution Modelling (SDM). This study presents results of our attempt to incorporate species competition in SDMs. We used a suit of predictor variables including Soil Adjusted Vegetation Index (SAVI), as well as distance from roads, settlements and water, fire frequency and distance from the nearest herbivore sighting (of selected herbivores) to model individual habitat preferences of five grazer species (buffalo, warthog, waterbuck, wildebeest and zebra) with the Ensemble SDM algorithm for Gonarezhou National Park, Zimbabwe. Our results showed that distance from the nearest animal sighting (a proxy for competition among grazers) was the best predictor of the potential distribution of buffalo, wildebeest and zebra but the second best predictor for warthog and waterbuck. Our findings p...

Remote means far away, therefore remote sensing as defined by Lillesand and Kiefer (1994) refers to the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation. Information extraction refers to the interpretation and extraction of qualitative and quantitative information from remotely sensed images. Information extraction methods from remote sensing imagery can be divided into two groups that is

Retrieval operations include the selective search on spatial and attribute data in such a way that geographical locations of features are not changed that is the output shows the selectively retrieved data in their geographic positions Goodchild (2003). The criteria for selective retrieval based on codes of attributes, Boolean logical condition or classification. In pixel information retrieval the pixel information window allows the reading of data from many maps and tables simultaneously, for the XY-coordinate that the mouse pointer has pointed. Retrieval also is done by displaying a map by one of its attributes. An attribute table should be linked to the map, and the column values that you show may use a representation. Both vector and raster maps that have a class domain, an identifier domain (ID domain) or the Unique ID domain, and that have an attribute table linked to it, have the possibility to display attributes instead of their own classes or IDs. Displaying a map by one of its attributes is useful to investigate the spatial distribution of attribute information, without the need of generating a new map. A mask can also be used to selectively display some classes, IDs or values of a map.

Wetland vegetation is an important bio-indicator of wetland physical or chemical degradation. Wetland vegetation continually faces threats from natural disturbance and unsustainable human activities. Traditionally, routine field observations are used to monitor wetlands. However, these methods are expensive and require a lot of human resources, as destructive sampling is required. Remote sensing offers non-destructive and real-time information useful for sustainable and effective management of wetland vegetation. The aim of this study was to explore the potential of hyperspectral remote sensing for wetland vegetation discrimination at the species level. In particular, the study focuses on enhancing or improving class separability among wetland vegetation species using hyperspectral data. In situ hyperspectral measurements were conducted on four dominant grass species in a wetland in Harare. One-way analysis of variance demonstrated significant statistical differences between hydrophytic vegetation species. Vegetation indices performed better compared to red-edge algorithms at discriminating wetland vegetation, with overall accuracy of 82% and 60%, respectively.