Understanding human‐wildlife conflict: a geographic study of the Pringle Bay chacma baboon troop
- Authors: Parsons, Wendy Jennifer
- Date: 2021-10-29
- Subjects: Chacma baboon South Africa Pringle Bay , Human-animal relationships South Africa Pringle Bay , Radio collars , Geographic information systems , Chacma baboon South Africa Pringle Bay Geographical distribution , Chacma baboon Behavior South Africa Pringle Bay , Chacma baboon Effect of human beings on South Africa Pringle Bay , Geospatial data , User-generated content
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/294828 , vital:57259
- Description: A better appreciation of the physical geography and environmental factors that play a role in the movement of the Chacma baboon troop in and around Pringle Bay (Overberg Municipality) and part of the Kogelberg Biosphere could lead to a better understanding of their movement. In turn, this insight may contribute to reducing the human‐wildlife conflict that has arisen in the town. Humanwildlife conflict escalated after the rapid urban development that followed the introduction of electricity in 1993. The baboon‐human conflict in Pringle Bay is, in part, due to habitat loss caused by urban development and the easy availability of food in the urban area. The wild animal’s natural behaviour (seeking food and fresh water) and the human way of living (food and waste management) has led to baboon habituation and increased raiding in the village. The objective of this geographic study was to understand the baboon troops spatial and temporal movements. Two methods are being used to track the baboon troop. The first method entails collection of data from GPS tracking collars which record the location of the baboons at 30 minute intervals. This is considered a reliable, but invasive and expensive method where the alpha male and female baboon had to be captured and fitted with tracking collars. The second method entails using volunteered geographic data, in this case, information from a WhatsApp baboon alert group. While this provided data at no real cost, the mining of the information was challenging and building a geodatabase was time consuming. However, this citizen science approach added valuable data and was able to identify human‐wildlife conflict sites in the urban area. The baboon location data was mapped using GIS. Primary and secondary spatial data was sourced and added to the geodatabase created in ArcMap 10.7. Various ArcMap tools were used in analysing the environmental factors (climate, vegetation, water sources and topography) together with the location data. Analysis of this data allowed the range of the baboons to be mapped, showing the maximum extent of the territory the baboons move in. The was refined by mapping their home range (defined as the area in which they spend 95% of the time) and their core area (in which they spend 50% of the time). High activity areas ‐ or hotspots ‐ were identified, as were the baboon sleep sites. The data allowed for habitat use and seasonal patterns of movement to be explored. A key finding of the research was that the baboons were observed outside of the urban area for 82% of the time. The baboons spent the majority of their time in mountain fynbos vegetation. Hotspot areas showing significant baboon activity were identified within the town and close correlation with their sleep sites and wetland areas was evident. No definitive seasonal or weather patterns were found that influence the baboon distribution. Baboon management is complex and difficult. The sustainability of the baboon troop is important for the biodiversity of the Kogelberg Biosphere Reserve. While the baboons should not be encouraged to enter the urban area, the residents should play a role in reducing the availability of food and baboonproofing their properties. The Overstrand Municipality also needs to address waste management and waste collection in the town. Understanding the biogeography of the baboons and implementing the above‐mentioned mitigating management measures would encourage human‐wildlife coexistence and inform future baboon management plans. , Thesis (MSc) -- Faculty of Science, Geography, 2021
- Full Text:
- Authors: Parsons, Wendy Jennifer
- Date: 2021-10-29
- Subjects: Chacma baboon South Africa Pringle Bay , Human-animal relationships South Africa Pringle Bay , Radio collars , Geographic information systems , Chacma baboon South Africa Pringle Bay Geographical distribution , Chacma baboon Behavior South Africa Pringle Bay , Chacma baboon Effect of human beings on South Africa Pringle Bay , Geospatial data , User-generated content
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/294828 , vital:57259
- Description: A better appreciation of the physical geography and environmental factors that play a role in the movement of the Chacma baboon troop in and around Pringle Bay (Overberg Municipality) and part of the Kogelberg Biosphere could lead to a better understanding of their movement. In turn, this insight may contribute to reducing the human‐wildlife conflict that has arisen in the town. Humanwildlife conflict escalated after the rapid urban development that followed the introduction of electricity in 1993. The baboon‐human conflict in Pringle Bay is, in part, due to habitat loss caused by urban development and the easy availability of food in the urban area. The wild animal’s natural behaviour (seeking food and fresh water) and the human way of living (food and waste management) has led to baboon habituation and increased raiding in the village. The objective of this geographic study was to understand the baboon troops spatial and temporal movements. Two methods are being used to track the baboon troop. The first method entails collection of data from GPS tracking collars which record the location of the baboons at 30 minute intervals. This is considered a reliable, but invasive and expensive method where the alpha male and female baboon had to be captured and fitted with tracking collars. The second method entails using volunteered geographic data, in this case, information from a WhatsApp baboon alert group. While this provided data at no real cost, the mining of the information was challenging and building a geodatabase was time consuming. However, this citizen science approach added valuable data and was able to identify human‐wildlife conflict sites in the urban area. The baboon location data was mapped using GIS. Primary and secondary spatial data was sourced and added to the geodatabase created in ArcMap 10.7. Various ArcMap tools were used in analysing the environmental factors (climate, vegetation, water sources and topography) together with the location data. Analysis of this data allowed the range of the baboons to be mapped, showing the maximum extent of the territory the baboons move in. The was refined by mapping their home range (defined as the area in which they spend 95% of the time) and their core area (in which they spend 50% of the time). High activity areas ‐ or hotspots ‐ were identified, as were the baboon sleep sites. The data allowed for habitat use and seasonal patterns of movement to be explored. A key finding of the research was that the baboons were observed outside of the urban area for 82% of the time. The baboons spent the majority of their time in mountain fynbos vegetation. Hotspot areas showing significant baboon activity were identified within the town and close correlation with their sleep sites and wetland areas was evident. No definitive seasonal or weather patterns were found that influence the baboon distribution. Baboon management is complex and difficult. The sustainability of the baboon troop is important for the biodiversity of the Kogelberg Biosphere Reserve. While the baboons should not be encouraged to enter the urban area, the residents should play a role in reducing the availability of food and baboonproofing their properties. The Overstrand Municipality also needs to address waste management and waste collection in the town. Understanding the biogeography of the baboons and implementing the above‐mentioned mitigating management measures would encourage human‐wildlife coexistence and inform future baboon management plans. , Thesis (MSc) -- Faculty of Science, Geography, 2021
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A review of generalized linear models for count data with emphasis on current geospatial procedures
- Authors: Michell, Justin Walter
- Date: 2016
- Subjects: Spatial analysis (Statistics) , Bayesian statistical decision theory , Geospatial data , Malaria -- Botswana -- Statistics , Malaria -- Botswana -- Research -- Statistical methods
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:5582 , http://hdl.handle.net/10962/d1019989
- Description: Analytical problems caused by over-fitting, confounding and non-independence in the data is a major challenge for variable selection. As more variables are tested against a certain data set, there is a greater risk that some will explain the data merely by chance, but will fail to explain new data. The main aim of this study is to employ a systematic and practicable variable selection process for the spatial analysis and mapping of historical malaria risk in Botswana using data collected from the MARA (Mapping Malaria Risk in Africa) project and environmental and climatic datasets from various sources. Details of how a spatial database is compiled for a statistical analysis to proceed is provided. The automation of the entire process is also explored. The final bayesian spatial model derived from the non-spatial variable selection procedure using Markov Chain Monte Carlo simulation was fitted to the data. Winter temperature had the greatest effect of malaria prevalence in Botswana. Summer rainfall, maximum temperature of the warmest month, annual range of temperature, altitude and distance to closest water source were also significantly associated with malaria prevalence in the final spatial model after accounting for spatial correlation. Using this spatial model malaria prevalence at unobserved locations was predicted, producing a smooth risk map covering Botswana. The automation of both compiling the spatial database and the variable selection procedure proved challenging and could only be achieved in parts of the process. The non-spatial selection procedure proved practical and was able to identify stable explanatory variables and provide an objective means for selecting one variable over another, however ultimately it was not entirely successful due to the fact that a unique set of spatial variables could not be selected.
- Full Text:
- Authors: Michell, Justin Walter
- Date: 2016
- Subjects: Spatial analysis (Statistics) , Bayesian statistical decision theory , Geospatial data , Malaria -- Botswana -- Statistics , Malaria -- Botswana -- Research -- Statistical methods
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:5582 , http://hdl.handle.net/10962/d1019989
- Description: Analytical problems caused by over-fitting, confounding and non-independence in the data is a major challenge for variable selection. As more variables are tested against a certain data set, there is a greater risk that some will explain the data merely by chance, but will fail to explain new data. The main aim of this study is to employ a systematic and practicable variable selection process for the spatial analysis and mapping of historical malaria risk in Botswana using data collected from the MARA (Mapping Malaria Risk in Africa) project and environmental and climatic datasets from various sources. Details of how a spatial database is compiled for a statistical analysis to proceed is provided. The automation of the entire process is also explored. The final bayesian spatial model derived from the non-spatial variable selection procedure using Markov Chain Monte Carlo simulation was fitted to the data. Winter temperature had the greatest effect of malaria prevalence in Botswana. Summer rainfall, maximum temperature of the warmest month, annual range of temperature, altitude and distance to closest water source were also significantly associated with malaria prevalence in the final spatial model after accounting for spatial correlation. Using this spatial model malaria prevalence at unobserved locations was predicted, producing a smooth risk map covering Botswana. The automation of both compiling the spatial database and the variable selection procedure proved challenging and could only be achieved in parts of the process. The non-spatial selection procedure proved practical and was able to identify stable explanatory variables and provide an objective means for selecting one variable over another, however ultimately it was not entirely successful due to the fact that a unique set of spatial variables could not be selected.
- Full Text:
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