In East Africa, honey bees (Apis mellifera) provide critical pollination services and income for small-holder farmers and rural families. While honey bee populations in North America and Europe are in decline, little is known about the status of honey bee populations in Africa. We initiated a nationwide survey encompassing 24 locations across Kenya in 2010 to evaluate the numbers and sizes of honey bee colonies, assess the presence of parasites (Varroa mites and Nosema microsporidia) and viruses, identify and quantify pesticide contaminants in hives, and assay for levels of hygienic behavior. Varroa mites were present throughout Kenya, except in the remote north. Levels of Varroa were positively correlated with elevation, suggesting that environmental factors may play a role in honey bee host-parasite interactions. Levels of Varroa were negatively correlated with levels of hygienic behavior: however, while Varroa infestation dramatically reduces honey bee colony survival in the US and Europe, in Kenya Varroa presence alone does not appear to impact colony size. Nosema apis was found at three sites along the coast and one interior site. Only a small number of pesticides at low concentrations were found. Of the seven common US/European honey bee viruses, only three were identified but, like Varroa, were absent from northern Kenya. The number of viruses present was positively correlated with Varroa levels, but was not correlated with colony size or hygienic behavior. Our results suggest that Varroa, the three viruses, and Nosema have been relatively recently introduced into Kenya, but these factors do not yet appear to be impacting Kenyan bee populations. Thus chemical control for Varroa and Nosema are not necessary for Kenyan bees at this time. This study provides baseline data for future analyses of the possible mechanisms underlying resistance to and the long-term impacts of these factors on African bee populations.

Bee viral ecology is a fascinating emerging area of research: viruses exert a range of effects on their hosts, exacerbate impacts of other environmental stressors, and, importantly, are readily shared across multiple bee species in a community. However, our understanding of bee viral communities is limited, as it is primarily derived from studies of North American and European Apis mellifera populations. Here, we examined viruses in populations of A. mellifera and 11 other bee species from 9 countries, across 4 continents and Oceania. We developed a novel pipeline to rapidly and inexpensively screen for bee viruses. This pipeline includes purification of encapsulated RNA/DNA viruses, sequence-independent amplification, high throughput sequencing, integrated assembly of contigs, and filtering to identify contigs specifically corresponding to viral sequences. We identified sequences for (+)ssRNA, (−)ssRNA, dsRNA, and ssDNA viruses. Overall, we found 127 contigs corresponding to novel viruses (i.e. previously not observed in bees), with 27 represented by >0.1% of the reads in a given sample, and 7 contained an RdRp or replicase sequence which could be used for robust phylogenetic analysis. This study provides a sequence-independent pipeline for viral metagenomics analysis, and greatly expands our understanding of the diversity of viruses found in bee communities.

BackgroundWith the development of inexpensive, high-throughput sequencing technologies, it has become feasible to examine questions related to population genetics and molecular evolution of non-model species in their ecological contexts on a genome-wide scale. Here, we employed a newly developed suite of integrated, web-based programs to examine population dynamics and signatures of selection across the genome using several well-established tests, including FST, pN/pS, and McDonald-Kreitman. We applied these techniques to study populations of honey bees (Apis mellifera) in East Africa. In Kenya, there are several described A. mellifera subspecies, which are thought to be localized to distinct ecological regions.ResultsWe performed whole genome sequencing of 11 worker honey bees from apiaries distributed throughout Kenya and identified 3.6 million putative single-nucleotide polymorphisms. The dense coverage allowed us to apply several computational procedures to study population structure and the evolutionary relationships among the populations, and to detect signs of adaptive evolution across the genome. While there is considerable gene flow among the sampled populations, there are clear distinctions between populations from the northern desert region and those from the temperate, savannah region. We identified several genes showing population genetic patterns consistent with positive selection within African bee populations, and between these populations and European A. mellifera or Asian Apis florea.ConclusionsThese results lay the groundwork for future studies of adaptive ecological evolution in honey bees, and demonstrate the use of new, freely available web-based tools and workflows (http://usegalaxy.org/r/kenyanbee) that can be applied to any model system with genomic information.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1712-0) contains supplementary material, which is available to authorized users.

The origin of the western honey bee Apis mellifera has been intensely debated. Addressing this knowledge gap is essential for understanding the evolution and genetics of one of the world's most important pollinators. By analyzing 251 genomes from 18 native subspecies, we found support for an Asian origin of honey bees with at least three expansions leading to African and European lineages. The adaptive radiation of honey bees involved selection on a few genomic "hotspots." We found 145 genes with independent signatures of selection across all bee lineages, and these genes were highly associated with worker traits. Our results indicate that a core set of genes associated with worker and colony traits facilitated the adaptive radiation of honey bees across their vast distribution. RESULTS Sequencing and variant detectionWe curated a genomic dataset of 251 individual A. mellifera samples representing 18 putative subspecies, of which 14 representative groups