Fadzai M. Zengeya

An IKONOS-based comparison of methods to estimate cattle home ranges in a semi-arid landscape of southern Africa

We estimated home range (HR) and core areas of cattle herds in a semi-arid rangeland in southern Africa using the fixed kernel and the local convex hull (LoCoH) methods. We also compared the HR values of the two methods with the aid of high spatial resolution IKONOS imagery. We also compared area estimates determined by the two methods at different probability contours of the utilization distribution (UD). Results showed that the LoCoH performed better than the kernel method in estimating UD. We found significant (p < 0.05) differences concerning the area estimated at each probability contour of the UD between the fixed kernel and LoCoH methods. However, both methods produced similar land cover preferences within the HR and core areas. Based on IKONOS-imagery-aided evaluation, our results imply that LoCoH determines core areas in HR analysis better than the kernel method, while both methods can be used in preference analysis.

Intraspecific variations in home range overlaps of a semi-free range herbivore are explained by remotely sensed productivity

In this study, we used remotely sensed Enhanced Vegetation Index (EVI) as a measure of food abundance to explain intraspecific home range overlap of Global Positioning System (GPS) collared semi-free range herbivores (Bos taurus). We then tested whether seasonality (estimated using the coefficient of variation of EVI) was related to intensity of home range overlap. We also tested whether the intensity of home range overlap was related to distance from water, particularly during the dry season. We determined the intensity of home range overlap using the Utilization Distribution Overlap Index. We then modelled the intensity of home range overlap as a function of seasonality. Results show that intensity of home range overlap varied with seasonality in a non-linear U-shaped manner. In addition, results showed that at both high and low levels of seasonality, intensity of home range overlap was high while it was low at moderate levels of seasonality. Results also indicated that the intensity of home range overlap increased with distance from water during the dry period. The U-shaped relationship obtained in this study conform to the behavioural theory that predicts similar relationships between food abundance and territoriality, indicating that remotely sensed productivity is an ecologically meaningful measure of food abundance. This further amplifies the utility of the combination of GPS animal movement data and remotely sensed data in spatial ecology.

Inference of herder presence from GPS collar data of semi-free range cattle

Although global positioning system (GPS) location data have been used to derive animal movement parameters including step length, rarely have these parameters been used to predict animal responses to human interventions. In this study, we tested whether GPS-derived step length of semi-free range cattle is a function of herder presence. The derived step-length model was used to predict herder presence on independent cattle GPS collar data. We also tested whether cattle foraging behaviour is explained by herder activity and vegetation greenness. We used logistic regression to model herder presence as a function of step length and relate cattle behaviour with herder activity and vegetation greenness. The field-based step length model successfully predicted herder presence on GPS collar data. The average predicted frequency of herder presence for the GPS-collared herds was 31%, whilst the field-based GPS frequency was 27%. Herding activities and vegetation greenness also explained different cattle foraging behaviour.

Elephant (Loxodonta africana) GPS collar data show multiple peaks of occurrence farther from water sources

Abstract The understanding of animal distribution in habitats located farther from water sources has not been dealt with adequately in the literature, yet this knowledge enables better prediction of species occurrence across an entire landscape. We tested whether elephant occurrence peaks away from water in addition to the known peak that is associated with water sources. We used the Maximum Entropy Modelling (MaxEnt) algorithm to predict the potential distribution of elephants in the Gonarezhou National Park, Zimbabwe. Elephant tracking data from Global Positioning System (GPS) collars were used as the response variable while NDVI (a proxy for forage quantity) and water sources data were the environmental variables. Results showed multiple peaks of elephant occurrence with increasing distance from water sources. Additionally, results illustrated that the peaks occur in high NDVI areas. Our findings emphasise the utility of GIS and remote sensing in enhancing our understanding of animal occurrence driven by water sources.

GIS-based stratification of malaria risk zones for Zimbabwe

Abstract Malaria burden has considerably declined in the last 15 years mainly due to large-scale vector control. The continued decline can be sustained through malaria risk stratification. Malaria stratification is the classification of geographical areas according to malaria risk. In this study, ecological niche modelling using the maximum entropy algorithm was applied to predict malaria vector habitat suitability in terms of bioclimatic and topographic variables. The output vector suitability map was integrated with malaria prevalence data in a GIS to stratify Zimbabwe into different malaria risk zones. Five improved and validated malaria risk zones were successfully delimited for Zimbabwe based on the World Health Organization classification scheme. These results suggest that the probability of occurrence of major vectors of malaria is a key determinant of malaria prevalence. The delimited malaria risk zones could be used by National Malaria Control programmes to plan and implement targeted malaria interventions based on vector control.

Mapping waterholes and testing for aridity using a remote sensing water index in a Southern African semi-arid wildlife area

Abstract Waterholes are a key resource that influences wildlife distribution in semi-arid ecosystems. Mapping waterholes can guide intervening decisions for supplementing water resources and managing wildlife distribution patterns. Although remote sensing provides a key to mapping distribution of waterholes, efficiency of existing remotely sensed methods for detecting waterholes have to be evaluated and even new ones developed. In this study, we evaluated performance of the Modified Normalized Difference Water Index (MNDWI) and Superfine Water Index (SWI) at selected optimum thresholds. Kappa results indicated that MNDWI detects waterholes better than SWI. We further validated MNDWI detected waterholes by testing response of waterhole area to temporal rainfall variability and waterhole persistence to spatial rainfall variability. Extent of MNDWI-detected waterholes varied in relation to temporal rainfall variability (p < 0.05). Waterhole persistence was not associated with spatial rainfall variability which could be explained by differences in waterhole types or low spatial rainfall variability.

Screening key browse species in a semi-arid rangeland

Abstract Rangeland productivity in semi-arid areas is adversely affected by increased variability in precipitation and frequency of droughts, coupled by increased livestock numbers. Knowledge on key rangeland resources that have capacity to increase resilience of livestock based rural livelihoods is critical for ensuring their sustainability. In this study, we identified key browse species used by livestock during the dry season, and determined their multiple uses in a semi-arid rangeland of Zimbabwe. Random sampling was used to select 138 respondents for participating in individual qualitative questionnaires, and seven key informants for a focus group discussion. The Cultural Significance Index was calculated to determine the importance of the key browse species identified. An index to determine risk associated with competitive use of key browse species based on individual species uses and relative abundance as an indicator for species sustainability was also introduced. Twenty-eight key species used as browse by livestock and wildlife, and for ethnoveterinary and human medicines were identified. Species that were common to all uses constituted 25% (n = 7) of the total. No species (n = 0) had a single purpose only or, were used for both medicines and firewood/timber. Therefore, screening key browse species facilitates their sustainability.