Evaluating occupancy and the range dynamics of invasive bird species in South Africa - Swingler, James (Department of Statistical Sciences, 2023)
There is great interest in the distribution of invasive species that threaten indigenous wildlife. All effective conservation management decisions need to be based on sound inference and predictions so that these species can be controlled and the risk posed to the local ecosystem minimized. Thus, there is significant benefit in the study of invasive species as a means of aiding those charged with protecting indigenous wildlife. The occupancy and population range dynamics of the Myna and Mallard species are individually investigated in the South African region by fitting static and dynamic occupancy models to a set of citizen science data for a 10-year study period between 2010-2019. The occupancy and detectability of the respective species is analysed using static occupancy models for the 2010 study season. The covariates included in the best fitting static models are used to estimate the initial occupancy and detection parameters for the dynamic models which now include estimates for colonization and local extinction. A sensitivity analysis pertaining to the dynamic models is implemented by altering the data structures in terms of the number of analysed sites and length of the detection histories. The results find the Myna's proximity to urban environments to play a significant role on its occupancy in 2010, and yearly changes in climatic and anthropogenic factors influence its 10-year range dynamics. The models fitted to the Mallard are inconclusive possibly due to the violation of the closure assumption potentially caused by migratory behaviour. The results are limited by the presence of a potentially migratory species when using a poorly designed study and highlights the difficulties of conducting an occupancy analysis on a highly mobile avian species as opposed to their sedentary counterpart. The workings of this dissertation support previous claims that an increase in the quantity of sites, and thus the degree of overlapping sites over the different seasons, will improve the precision of the model estimates. However, caution must be exercised when increasing the length of the seasonal detection histories and should generally be set to no more than 10 repeated visits to a site.