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

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

Tracking data highlight the importance of human-induced mortality for large migratory birds at a flyway scale

Human-induced direct mortality affects huge numbers of birds each year, threatening hundreds of species worldwide. Tracking technologies can be an important tool to investigate temporal and spatial patterns of bird mortality as well as their drivers. We compiled 1704 mortality records from tracking studies across the African-Eurasian flyway for 45 species, including raptors, storks, and cranes, covering the period from 2003 to 2021. Our results show a higher frequency of human-induced causes of mortality than natural causes across taxonomic groups, geographical areas, and age classes. Moreover, we found that the frequency of human-induced mortality remained stable over the study period. From the human-induced mortality events with a known cause (n = 637), three main causes were identified: electrocution (40.5 %), illegal killing (21.7 %), and poisoning (16.3 %). Additionally, combined energy infrastructure-related mortality (i.e., electrocution, power line collision, and wind-farm collision) represented 49 % of all human-induced mortality events. Using a random forest model, the main predictors of human-induced mortality were found to be taxonomic group, geographic location (latitude and longitude), and human footprint index value at the location of mortality. Despite conservation efforts, human drivers of bird mortality in the African-Eurasian flyway do not appear to have declined over the last 15 years for the studied group of species. Results suggest that stronger conservation actions to address these threats across the flyway can reduce their impacts on species. In particular, projected future development of energy infrastructure is a representative example where application of planning, operation, and mitigation measures can enhance bird conservation

The distribution of cattle and their interaction with the African Buffalo at the wildlife-livestock interface understood using real-time Global Positioning Systems (GPS) and remotely sensed data

The distribution of herbivores is hypothesized to be influenced by an interplay of several biophysical factors as well as human factors. However, our current understanding of herbivore movement dynamics and factors driving the movement and interaction between domestic and wild herbivores at interfaces between wildlife and livestock areas is limited. In this regard, the development of methods and approaches that allow spatially explicit (1) continuous monitoring, and (2) prediction of animal movements in relation to biophysical and human factors is critical for an improved understanding of livestock and wildlife distribution at these interfaces. The advent of Global Positioning Systems (GPS), remote sensing and improved spatial analytical frameworks has provided opportunities to accomplish this task. In this thesis, we develop novel methods and approaches using GPS and remote sensing to understand factors influencing the distribution of livestock, i.e. cattle (Bos taurus) and their spatial overlap with wild herbivores i.e. the African buffalo (Syncerus caffer) at a wildlife-livestock interface. Results showed that the distribution of GPS collared cattle was closely linked with the spatial variation of remotely sensed foliar nitrogen signifying for the first time that new advanced multispectral sensors, particularly WorldView-2 could be used for predicting and mapping forage quality. Results also showed that cattle are central place foragers as distance from the kraal influenced habitat selection. This is the first time that the central place effect has been included in habitat selection models for large herbivores. In this thesis results showed that habitat composition explained shifts in space use as both the home range and core areas shifted in response to variations in habitat composition. Results showed that herbivores responded differently to the effects of landscape structure and landscape productivity depending on season (wet or dry). In fact, only landscape structure explained wet season cattle distribution while both landscape structure and landscape productivity influenced dry season distribution. For the first time in thesis, we showed that intensity of spatial overlap between cattle, a non-territorial species, conformed to that predicted for territorial species where overlap is high at both low and high levels of food abundance whilst low at intermediate levels. This indicates that the influence of food on intraspecific overlaps is species-independent, thus, allowing for wider applicability of the model. Moreover, results showed that using GPS-derived movement metrics, deviations in animal behaviour and movement could be modeled as a function of herder presence. This finding opens unprecedented possibilities for monitoring deviations in animal behaviour using GPS. We demonstrated that resource (vegetation greenness) gradients exist at wildlife-livestock interfaces and that these gradients drive livestock movement into conservation areas resulting in overlaps with the African buffalo. We also found that during resource limited periods both spatial aggregation and segregation occurred between cattle and buffalo, indicating potential resource competition. Overall, in this thesis we have demonstrated the potential of combining GPS data, remote sensing and spatial analysis in advancing our understanding of movement dynamics and factors influencing the distribution of livestock and their spatial overlap with wildlife at a wildlife-livestock interface

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

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

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

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

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

Mapping key browse resources in a heterogeneous agricultural landscape

We investigated application of MaxEnt, a one-class classifier, in mapping the spatial distribution of Colophospermum mopane, Dichrostachys cinerea and Salvadora persica using drainage, elevation, slope, soil and Normalised Difference Vegetation Index as environmental variables. Model performance was evaluated based on the area under the ROC curve (AUC), Kappa and Total Skills Statistic. The AUC results demonstrated the highpredictive power of MaxEnt as test data values of all species, respectively, were 0.694, 0.754 and 0.998 (p &lt; 0.05). Elevation contributed the most in explaining spatial distributions of all species. Results also indicated that several one-class species maps can be integrated into one species distribution map. We showed that C. mopane is likely to co-occur with D. cinerea and S. persica, whereas S. persica is likely to co-occur with D. cinerea. However, there was no habitat suitable for co-occurrence of all species. One-class species mapping can therefore be successful in heterogeneous agricultural landscapes. Keywords: key browse species one class classification, spatial distributio

Application of maximum entropy (MaxEnt) to understand the spatial dimension of human–wildlife conflict (HWC) risk in areas adjacent to Gonarezhou National Park of Zimbabwe

The application of empirical and spatially explicit information to understand the spatial distribution of human–wildlife conflict (HWC) risk zones is increasingly becoming imperative to guide conservation planning and device mechanisms to enhance and sustain the coexistence between wildlife and humans. Spatial information on HWC is scarce in the literature, and previous studies have tended to concentrate more on the human dimensions of HWC. Although normally applied in wildlife studies, species distribution modeling (SDM) is becoming an indispensable tool to predict and visualize potential risk zones for HWC. In this study, we used maximum entropy (MaxEnt), a presence-only SDM to predict the potential distribution of HWC risk zones and to determine ecological variables that significantly explain the spatial distribution of HWC occurrences around the Gonarezhou National Park (GNP) in southeastern Zimbabwe. Our results show that HWC risk zones are not randomly distributed but tend to be concentrated along areas adjacent to protected areas that support potential overlaps and contacts between wildlife and human landscapes. A distinctive HWC high-risk zone is observed north of GNP, around areas such as Chitsa, Mpinga, and Masekesa—communities that should be prioritized for proactive mitigation interventions. In view of limited conservation resources typical of less developed countries, wildlife managers are pressed to explicitly determine zones with the highest HWC risks for effective and targeted interventions. Findings from this study thus provide a crucial baseline for identifying potentially high-risk HWC zones and the main predictors, knowledge that can be streamlined for proactive resource allocation to mitigate the HWC challenge

GIS-based stratification of malaria risk zones for Zimbabwe

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