Modelling the spatial-temporal distribution of tsetse (Glossina pallidipes) as a function of topography and vegetation greenness in the Zambezi Valley of Zimbabwe

Abstract

In this study, we developed a stable and temporally dynamic model for predicting tsetse (Glossina pallidipes) habitat distribution based on a remotely sensed Normalised Difference Vegetation Index (NDVI), an indicator of vegetation greenness, and topographic variables, specifically, elevation and topographic position index (TPI). We also investigated the effect of drainage networks on habitat suitability of tsetse as well as factors that may influence changes in area of suitable tsetse habitat. We used data on tsetse presence collected in North western Zimbabwe during 1998 to develop a habitat prediction model using Maxent (Training AUC = 0.751, test AU = 0.752). Results of the Maxent model showed that the probability of occurrence of tsetse decreased as TPI increased while an increase in elevation beyond 800 m resulted in a decrease in the probability of occurrence. High probabilities (>50%) of occurrence of tsetse were associated with NDVI between high 0.3 and 0.6. Based on the good predictive ability of the model, we fitted this model to environmental data of six different years, 1986, 1991, 1993, 2002, 2007 and 2008 to predict the spatial distribution of tsetse presence in those years and to quantify any trends or changes in the tsetse distribution, which may be a function of changes in suitable tsetse habitat. The results showed that the amount of suitable tsetse habitat significantly decreased (r2 0.799, p = 0.007) for the period 1986 and 2008 due to the changes in the amount of vegetation cover as measured by NDVI over time in years. Using binary logistic regression, the probability of occurrence of suitable tsetse habitat decreased with increased distance from drainage lines. Overall, results of this study suggest that temporal changes in vegetation cover captured by using NDVI can aptly capture variations in habitat suitability of tsetse over time. Thus integration of remotely sensed data and other landscape variables enhances assessment of temporal changes in habitat suitability of tsetse which is crucial in the management and control of tsetse.