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:
- Date Issued: 2021-10-29
- 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:
- Date Issued: 2021-10-29
Understanding the contribution of Land Use/Cover (LUC) classes on soil erosion and sedimentation using sediment fingerprinting technique and RUSLE in a GIS interface at sub-catchment level
- Taeni, Thembalethu (https://orcid.org/ 0000-0001-7662-8652)
- Authors: Taeni, Thembalethu (https://orcid.org/ 0000-0001-7662-8652)
- Date: 2021-04
- Subjects: Geographic information systems , Soil erosion , River sediments
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/20920 , vital:46747
- Description: Soil erosion by water is the major source of soil degradation in the world, and South Africa (SA) is not an exception particularly in the Free State (FS) Province. In South Africa, the Caledon River Catchment in the FS Province has been identified as one of the regions where soil erosion has been prevalent for decades. Evidence across many parts of the catchment show a widespread of soil erosion and the contaminant flux associated with sediment into river systems and reservoirs; including the Welbedatcht dam and Carthcart-drift dam in Ladybrand. It is of these issues that the current work aimed at enhancing the understanding of sediment sources and soil erosion dynamics at the Caledon River Basin. The objectives of the study were to locate sources of suspended sediments and to assess and quantify the contribution of Land Use Cover (LUC) classes to water erosion and sediment yield at a sub – catchment level of the Caledon River Basin. To achieve the objectives set out for this research, a study was conducted at a sub - catchment level of the Caledon River Basin in the FS Province, South Africa. The sediment–fingerprinting approach and the Revised Universal Soil Loss Equation (RUSLE) model were used in the study under Geographic Information System (GIS) settings. A qualitative and quantitative interpretation of the geochemical data were used to evaluate the potential for distinguishing catchment sediment sources. The application of multivariate sediment mixing models incorporating Monte Carlo simulations was undertaken to investigate recent variations in sediment sources. Lastly, to document the impact of LUC change on soil erosion; data from soil profile database, Landsat 8 OLI–TIRS and climate (i.e. rainfall) were used to assess and map the spatial and temporal pattern changes of soil erosion at a sub – catchment level as related to LUC changes. In this study, the sub–catchment was classified into 6 LUC classes. Thereafter soil erosion was quantified for three consecutive years namely; 2015, 2016 and 2018 using the soil erosion factors as GIS–layers. The investigation of sediment source types and spatial provenance in the catchment showed that the grassland areas have consistently been the main sediment source (83 percent) throughout the study period. Findings further showed that there was an increase in contributions from cultivation and abandoned cultivated fields. Sediment contribution from surface sources was dominant (54 percent) and thereafter, subsurface sediment input increased (62 percent). This trend is indicative of increased severity of gully erosion in the area and thus is consistent with other studies. To comprehend the influence of LUC class modification dynamics on soil erosion, water erosion in particular at the sub-catchment commencing from 2015 to 2018 (4 years), multi-temporal Landsat 8 information jointly with the RUSLE model were used. A post-classification, LUC class alteration comparison revealed that water bodies, shrubs and forested region and grassland declined by 0.27 percent, 15.60 percent, and 37.60 percent, respectively. On the other hand, regions under Bad lands, and bare-soil and built-up regions including agricultural region expanded by 2.22 percent, 5.78 percent, and 45.67 percent respectively, between 2015 to 2018 study period. The average yearly soil loss decreased at the sub-catchment and was 10.23,5.71 and 5.82 t ⋅ ha -1 ⋅ yr-1 for 2015, 2016 and 2018 respectively. Although soil loss lessened for the duration of the perceived period, a closer scrutiny revealed that there were nonetheless seeming signs of persistent escalation in soil loss risk. These signs were mostly shown in the elevated parts of the sub-catchment as shown by the red regions on the soil loss map. Additional examination of soil loss findings by LUC classes categories further indicated that most LUC classes categories, including Bare-soil and built-up area, agricultural-land, grassland, and region under shrubs and forests, showed increased soil loss levels during the 4 years’ study period at the sub-catchment. The information on the comparative vividness of diverse sediment sources given by the study must be observed as a noteworthy development towards an understanding of the sediment source dynamics in agricultural river based catchments; more so of the Caledon River Basin. Further research is recommended for other erosion prone catchments in South Africa to identify additional evidence of the spatial and temporal variations in soil erosion and sediment sources. The results of the study suggest that the procedure of assimilating the GIS and RS with the RUSLE model is not just precise, time-efficient and exact in recognizing soil erosion susceptible regions in geospatial and temporal standings. However, it is a cost-efficient substitute to standard field-founded approaches. , Thesis (MSc) (Soil Science) -- University of Fort Hare, 2021
- Full Text:
- Date Issued: 2021-04
- Authors: Taeni, Thembalethu (https://orcid.org/ 0000-0001-7662-8652)
- Date: 2021-04
- Subjects: Geographic information systems , Soil erosion , River sediments
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/20920 , vital:46747
- Description: Soil erosion by water is the major source of soil degradation in the world, and South Africa (SA) is not an exception particularly in the Free State (FS) Province. In South Africa, the Caledon River Catchment in the FS Province has been identified as one of the regions where soil erosion has been prevalent for decades. Evidence across many parts of the catchment show a widespread of soil erosion and the contaminant flux associated with sediment into river systems and reservoirs; including the Welbedatcht dam and Carthcart-drift dam in Ladybrand. It is of these issues that the current work aimed at enhancing the understanding of sediment sources and soil erosion dynamics at the Caledon River Basin. The objectives of the study were to locate sources of suspended sediments and to assess and quantify the contribution of Land Use Cover (LUC) classes to water erosion and sediment yield at a sub – catchment level of the Caledon River Basin. To achieve the objectives set out for this research, a study was conducted at a sub - catchment level of the Caledon River Basin in the FS Province, South Africa. The sediment–fingerprinting approach and the Revised Universal Soil Loss Equation (RUSLE) model were used in the study under Geographic Information System (GIS) settings. A qualitative and quantitative interpretation of the geochemical data were used to evaluate the potential for distinguishing catchment sediment sources. The application of multivariate sediment mixing models incorporating Monte Carlo simulations was undertaken to investigate recent variations in sediment sources. Lastly, to document the impact of LUC change on soil erosion; data from soil profile database, Landsat 8 OLI–TIRS and climate (i.e. rainfall) were used to assess and map the spatial and temporal pattern changes of soil erosion at a sub – catchment level as related to LUC changes. In this study, the sub–catchment was classified into 6 LUC classes. Thereafter soil erosion was quantified for three consecutive years namely; 2015, 2016 and 2018 using the soil erosion factors as GIS–layers. The investigation of sediment source types and spatial provenance in the catchment showed that the grassland areas have consistently been the main sediment source (83 percent) throughout the study period. Findings further showed that there was an increase in contributions from cultivation and abandoned cultivated fields. Sediment contribution from surface sources was dominant (54 percent) and thereafter, subsurface sediment input increased (62 percent). This trend is indicative of increased severity of gully erosion in the area and thus is consistent with other studies. To comprehend the influence of LUC class modification dynamics on soil erosion, water erosion in particular at the sub-catchment commencing from 2015 to 2018 (4 years), multi-temporal Landsat 8 information jointly with the RUSLE model were used. A post-classification, LUC class alteration comparison revealed that water bodies, shrubs and forested region and grassland declined by 0.27 percent, 15.60 percent, and 37.60 percent, respectively. On the other hand, regions under Bad lands, and bare-soil and built-up regions including agricultural region expanded by 2.22 percent, 5.78 percent, and 45.67 percent respectively, between 2015 to 2018 study period. The average yearly soil loss decreased at the sub-catchment and was 10.23,5.71 and 5.82 t ⋅ ha -1 ⋅ yr-1 for 2015, 2016 and 2018 respectively. Although soil loss lessened for the duration of the perceived period, a closer scrutiny revealed that there were nonetheless seeming signs of persistent escalation in soil loss risk. These signs were mostly shown in the elevated parts of the sub-catchment as shown by the red regions on the soil loss map. Additional examination of soil loss findings by LUC classes categories further indicated that most LUC classes categories, including Bare-soil and built-up area, agricultural-land, grassland, and region under shrubs and forests, showed increased soil loss levels during the 4 years’ study period at the sub-catchment. The information on the comparative vividness of diverse sediment sources given by the study must be observed as a noteworthy development towards an understanding of the sediment source dynamics in agricultural river based catchments; more so of the Caledon River Basin. Further research is recommended for other erosion prone catchments in South Africa to identify additional evidence of the spatial and temporal variations in soil erosion and sediment sources. The results of the study suggest that the procedure of assimilating the GIS and RS with the RUSLE model is not just precise, time-efficient and exact in recognizing soil erosion susceptible regions in geospatial and temporal standings. However, it is a cost-efficient substitute to standard field-founded approaches. , Thesis (MSc) (Soil Science) -- University of Fort Hare, 2021
- Full Text:
- Date Issued: 2021-04
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