Browsing by Author "Mudereri, Bester Tawona"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Modeling the geographic spread and proliferation of invasive alien plants (IAPs) into new ecosystems using multi-source data and multiple predictive models in the Heuningnes catchment, South Africa
    (Taylor and Francis, 2021) Mtengwana, Bhongolethu; Dube, Timothy; Mudereri, Bester Tawona
    The geographic spread and proliferation of Invasive Alien Plants (IAPs) into new ecosystems requires accurate, constant, and frequent monitoring particularly under the changing climate to ensure the integrity and resilience of affected as well as vulnerable ecosystems. This study thus aimed to understand the distribution and shifts of IAPs and the factors influencing such distribution at the catchment scale to minimize their risks and impacts through effective management. Three machine learning Species Distribution Modeling (SDM) techniques, namely, Random Forest (RF), Maximum Entropy (MaxEnt), Boosted Regression Trees (BRT) and their respective ensemble model were used to predict the potential distribution of IAPs within the catchment. The current and future bioclimatic variables, environmental and Sentinel-2 Multispectral Instrument satellite data were used to fit the models to predict areas at risk of IAPs invasions in the Heuningnes catchment, South Africa. The present and two future climatic scenarios from the Community Climate System Model (CCSM4) were considered in modeling the potential distribution of these species. The two future scenarios represented the minimum and maximum atmospheric carbon Representative Concentration Pathways (RCP) 2.6 and 8.5 for 2050 (average for 2041–2060).
  • Thumbnail Image
    Item
    Understanding climate change effects on the potential distribution of an important pollinator species, Ceratina moerenhouti (Apidae: Ceratinini), in the Eastern Afromontane biodiversity hotspot, Kenya
    (Elsevier, 2023) Mukundamago, Mukundi; Dube, Timothy; Mudereri, Bester Tawona
    Monitoring key pollinator taxa such as the genus Ceratina requires precise near real-time predictions to facilitate better surveillance. The potential habitat suitability of Ceratina moerenhouti was predicted in the Eastern Afromontane biodiversity hotspot (EABH) in Kenya using presence-only data, to identify their potential distribution and vulnerability due to climate change. Bioclimatic, edaphic, terrain, land surface temperature, and land use and land cover (LULC) variables were used as predictors. Three machine learning techniques, together with their ensemble model, were evaluated on their suitability to predict current and future (the shared socioeconomic pathways (SSPs), i.e., SSP245 and SSP585) habitat suitability. Predictors were subjected to variable selection using the variance inflation factor resulting in a few (n = 9) optimum variables. The area under the curve (AUC) and true skill statistic (TSS) were used for the accuracy assessment of the modeling outputs. The results indicated that 30% and 10% of the EABH in Murang’a and Taita Taveta counties are currently suitable for C. moerenhouti occurrence, respectively. However, future projections show a ±5% decrease in C. moerenhouti habitats in the two counties. Further, the ensemble model harnessed the algorithm differences while the random forest had the highest individual predictive power (AUC = 0.97; TSS = 0.96). Clay content, LULC, and the slope were the most relevant variables together with temperature and precipitation. Integrating multi-source data in predicting suitable habitats improves model prediction capacity. This study can be used to support the maintenance of flowering plant communities around agricultural areas to improve pollination services.
  • Thumbnail Image
    Item
    Understanding Striga occurrence and risk under changing climatic conditions across different agroecological farming systems at local and regional scales122
    (University of Western Cape, 2020) Mudereri, Bester Tawona; Dube, Timothy
    The invasion by Striga in most cereal crop fields in Africa has posed an acute threat to food security and socioeconomic integrity. Consequently, numerous technological and research developments have been made to minimize and even control the Striga impacts on crop production. So far, efforts to control Striga have primarily focused on the manipulation of the genetics of the host crops, as well as understanding the phenological and physiological traits, along with the chemical composition of the weed.