Uganda has experienced 4 Ebola outbreaks since the discovery of the virus. Recent epidemiological work has shown pigs are hosts for Ebola viruses. Due to their high reproduction rates, rapid weight gain, potential to provide quick financial returns and rising demand for pork, pig production in Uganda has undergone massive expansion. The combination of pork sector growth supported by development programmes and Ebola virus risk prompted a foresight exercise using desk, interview and spatial methods. The study found that the lack of serological evidence for specific reservoir species, the number of human index cases unable to account for their source of infection, domestic pig habitat overlap with potential Ebola virus zoonotic host environments, reported interactions at the human-pig-wildlife interface that could support transmission, fever in pigs as a commonly reported problem by pig farmers and temporal correlation of outbreaks with peak pork consumption periods warrants further research into potential zoonotic transmission in Uganda from pigs.

Landscape fragmentation and habitat loss at multiple scales directly affect species abundance, diversity, and productivity. There is a paucity of information about the effect of the landscape structure and diversity on honey bee colony strength in Africa. Here, we present new insights into the relationship between landscape metrics such as patch size, shape, connectivity, composition, and configuration and honey bee (Apis mellifera) colony strength characteristics. Remote‐sensing‐based landscape variables were linked to honey bee colony strength variables in a typical highly fragmented smallholder agroecological region in Kenya. We examined colonies in six sites with varying degrees of land degradation during the period from 2017 to 2018. Landscape structure was first mapped using medium resolution bitemporal Sentinel‐1 and Sentinel‐2 satellite imagery with an optimized random forest model. The influence of the surrounding landscape matrix was then constrained to two buffer distances, i.e., 1 km representing the local foraging scale and 2.5 km representing the wider foraging scale around each investigated apiary and for each of the six sites. The results of zero‐inflated negative binomial regression with mixed effects showed that lower complexity of patch geometries represented by fractal dimension and reduced proportions of croplands were most influential at local foraging scales (1 km) from the apiary. In addition, higher proportions of woody vegetation and hedges resulted in higher colony strength at longer distances from the apiary (2.5 km). Honey bees in moderately degraded landscapes demonstrated the most consistently strong colonies throughout the study period. Efforts towards improving beekeeper livelihoods, through higher hive productivity, should target moderately degraded and heterogeneous landscapes, which provide forage from diverse land covers.

The region of Southern Ethiopia (Borana) and Northern Kenya (Marsabit) is characterised by erratic rainfall, limited surface water, aridity, and frequent droughts. An important adaptive response to these conditions, of uncertain antiquity, has been the hand-excavation of a sequence of deep wells at key locations often along seasonal riverbeds and valley bottoms where subterranean aquifers can be tapped. Sophisticated indigenous water management systems have developed to ensure equitable access to these critical water resources, and these are part of well-defined customary institutional leadership structures that govern the community giving rise to a distinctive form of biocultural heritage. These systems, and the wells themselves, are increasingly under threat, however, from climate change, demographic growth, and socio-economic development. To contribute to an assessment of the scale, distribution and intensity of these threats, this study aimed to evaluate the land-use land-cover (LULC) and precipitation changes in this semi-arid to arid landscape and their association with, and impact on, the preservation of traditional wells. Multitemporal Landsat 5, 7 and 8 satellite imagery covering the period 1990 to 2020, analysed at a temporal resolution of 10 years, was classified using supervised classification via the Random Forest machine learning method to extract the following classes: bare land, grassland, shrub land, open forest, closed forest, croplands, settlement and waterbodies. Change detection was then applied to identify and quantify changes through time and landscape degradation indices were generated using the Shannon Diversity Index fragmentation index within a 15 km buffer of each well cluster. The results indicated that land cover change was mostly driven by increasing anthropogenic changes with resultant reduction in natural land cover classes. Furthermore, increased fragmentation has occurred within most of the selected buffer distances of the well clusters. The main drivers of change that have directly or indirectly impacted land degradation and the preservation of indigenous water management systems were identified through an analysis of land cover changes in the last 30 years, supporting insights from previous focused group discussions with communities in Kenya and Ethiopia. Our approach showed that remote sensing methods can be used for the spatially explicit mapping of landscape structure around the wells, and ultimately towards assessment of the preservation status of the indigenous wells.

Extensive land transformation leads to habitat loss, which directly affects and fragments species habitats. Such land transformations can adversely affect fodder availability for bees and thus colony strength with consequences for rural communities that use bee keeping as a livelihood option. Quantification of the landscape structure is thus critical if the linkages between the landscape and honey bee colony health are to be well understood. In this study, a random forest algorithm was used on dual-polarized multi-season Sentinel-1A (S1) synthetic aperture radar (SAR) and single season Sentinel-2A (S2) optical imagery to map honey bee habitats and their degree of fragmentation in a heterogeneous agro-ecological landscape in eastern Kenya. The dry season S2 optical imagery was fused with the S1 data and class-wise mapping accuracies (with and without radar) were compared. Relevant fragmentation indices representing patch sizes, isolation and configuration were thereafter generated using the fused imagery. The fused imagery recorded an overall accuracy of 86% with a kappa of 0.83 versus the SAR imagery only, which had an overall accuracy of 76% with a kappa of 0.68. However, the S1 imagery had slightly higher user’s and producer’s accuracies for under-represented but important honey bee habitat classes, i.e. natural grasslands and hedges. The variable importance analysis using the fused imagery showed that the short-wave infrared (SWIR) and the red-edge (RE) waveband regions were highly relevant for the classification model. Our mapping approach showed that fusing data generated from S1 and S2 with improved spectral resolution, could be effectively used for the spatially explicit mapping of honey bee habitats and their degree of fragmentation in semi-arid African agro-ecological landscapes.

In Africa there is a scarcity of information on how plant species that can provide forage for honey bees vary across differentially structured landscapes, and what are the implications of such variabilities on colony integrity. This research presents new insights into the diversity and richness of pollen collected by Apis mellifera scutellata, a subspecies of the Western honey bee native to sub-Saharan Africa, at six study sites of different degradation levels within a semi-arid landscape in Kenya. Ten colonies were established at each site and land cover characteristics were extracted using novel remote sensing methods. The sites differed by the proportions of natural vegetation, cropland, grassland and hedges within each site. Bee bread was collected five times, with three colonies in each of the six sites repeatedly sampled during the period from May 2017 to November 2018. Pollen identification and protein analysis within the study sites were thereafter conducted to establish the linkage between landscape degradation levels and abundance and diversity of pollen. Out of 124 plant species identified, Terminalia spp., Cleome spp. and Acacia spp. were identified as the most abundant species. Moreover, species richness and diversity were highest in the two sites located in moderately degraded landscapes. Pollen protein content showed statistically significant differences across season rather than geographical location. This study demonstrated that landscape degradation negatively affected the diversity and richness of pollen collected by honey bees. Consequently, this helps our understanding of native honey bees’ forage resource usage and plant species preferences in landscapes with varying degrees of degradation.