Doctoral Degrees (Conservation Ecology and Entomology)

Permanent URI for this collection

https://scholar.sun.ac.za/handle/10019.1/531

Browse

Browsing Doctoral Degrees (Conservation Ecology and Entomology) by browse.metadata.advisor "Addison, Matthew"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Spatio-temporal analyses of fruit fly populations in selected areas of the Western Cape
    (Stellenbosch : Stellenbosch University, 2019-02) Bekker, Gerard Francois Hermanus van Ginkel; Addison, Pia; Addison, Matthew; Van Niekerk, Adriaan; Stellenbosch University. Faculty of AgriSciences. Dept. of Conservation Ecology and Entomology.
    ENGLISH ABSTRACT: The aim of this study was to investigate the spatio-temporal distribution of Ceratitis capitata (Wiedemann) (Diptera: Tephritidae) populations in heterogeneous fruit producing environments in Western Cape, South Africa, using geospatial analyses and machine learning (ML) techniques. A small scale study was conducted at orchard level on the Welgevallen experimental farm in Stellenbosch, South Africa, investigating the spatial patterns and associations of C. capitata and Ceratitis quilicii females, another important fruit fly pest in the Western Cape. The females of both species had aggregated spatial patterns, but their temporal patterns differed, with C. capitata aggregating significantly more towards the end of the season while C. quilicii aggregated significantly towards the beginning of the season. Ceratitis capitata and C. quilicii females were spatially associated, most prominently in home gardens, natural vegetation, citrus and nectarines. A geographical database was developed, incorporating existing area-wide trap monitoring data for C. capitata populations in the Elgin/Grabouw, Villiersdorp, Vyeboom (EGVV) region, Western Cape, an area currently under Sterile Insect Technique (SIT) management, was used to develop a geographical database with the aim to investigate the area-wide spatiotemporal distribution of C. capitata. There were no definitive spatial distribution pattern of C. capitata across all seasons. However, through visual analyses of spatial maps, a southeast/north-west split was observed where traps in the south-eastern parts of the study area showed higher catches and traps in the north-western parts showed lower catches. The results suggested a relationship between the geographic characteristics of EGVV and the abundance and distribution of C. capitata populations. The relationship between the geographic characteristics of the study area (EGVV) and the spatio-temporal distribution of C. capitata were further investigated using ML techniques. Monthly and seasonal long-term C. capitata spatio-temporal distributions were quantified into hot-and cold spots (HCSs), using spatial analyses tools. HCSs were then related to a set of geographic variables, using the random forest (RF) ML classification algorithm to determine the main drivers of the HCSs for C. capitata in the EGVV region. Spatial analyses showed that hot spots were concentrated in the hotter and drier areas, while cold spots were concentrated in the colder and wetter areas. The RF results indicated that rainfall was the most important driver of the HCSs in the EGVV region. To test the robustness of the RF algorithm for the purpose of explaining C. capitata HCSs in a heterogeneous fruit producing environment, the sample size and the variability in the geographic variables were increased by combining data from two regions: the EGVV and the Warmbokkeveld (WB), another fruit producing region under SIT. RF model accuracies from the combined dataset were not significantly lower than those of the individual regions. The drivers of C. capitata spatial distribution were different between regions, but distance to urban areas in the early fruiting season emerged as a strong driver in all scenarios. The findings showed that RF is a useful tool for investigating the spatio-temporal distribution of area-wide tephritid fruit fly trapping data, and that it can handle complex classification problems. It was evident from this study that the spatio-temporal distribution of C. capitata populations are driven by area-specific geographic variables. The area-specific RF models provided invaluable information, which could be used to improve the planning and implementation of area-wide C. capitata management programmes in heterogeneous agricultural landscapes. This study is relevant to the integrated management of fruit flies and potentially other insect pest species, on a local and regional scale. The framework which was developed will allow for the integration of a variety of data and the resultant analyses are relevant at an orchard and regional level. The information will assist efficient decision making by farmers and managers of area-wide integrated pest management programmes.