Design of breeding programs to improve honeybee health and production

This PhD project will design breeding programs to improve honeybee health and production through a unique collaboration between researchers, practitioners, beekeepers and bee-farmers.

Honeybees are under pressure due to pests and environmental changes. There are competing views on what type of a honeybee are best suited different environmental conditions and management practices. These views are largely based on bee morphology and productivity traits, with limited quantitative evidence and analysis.

The issue can be addressed via a three-pronged approach of
(i) collecting quantitative evidence for different types of bees, conditions and practices, (ii) associating phenotypic variation to said evidence and (iii) designing modern, efficient and sustainable data-driven breeding programs.

To this end the project will be organised in three work-packages.

First, quantitative evidence will be collected from a sample of colonies in the UK and/or New Zealand. The focal phenotype traits will be health status and production along with the state-of-the-art high-frequency and high-volume Internet-of-things (IoT) colony sensors and meta-data (management, location, and weather). In addition, colonies will be whole-genome sequenced.

Second, the collected data will be analysed. Specifically, regressing phenotypes on bee morphology, whole-genome sequence variants, management, location and weather descriptors. The genome and location will be modelled by accounting phylogenetic and spatial relationships to disentangle genetic and environmental variation. In addition, associations with IoT colony data will be evaluated with machine learning to build predictive models for health status. These results will provide quantitative evidence about differences due to genetic, management and environmental effects.

Third, the results from the first two work-packages will be used to build an in-silico honeybee breeding framework. This framework will be used to engineer most suitable breeding programs for different conditions and practices. Particular attention will be given to spatial distribution and therefore unavoidable hybridization between different bee types. Further, emphasis will be given to design sustainable breeding programs (in terms of genetic diversity) that can address current and future challenges. In summary the outcome will be a set of practical breeding schemes that will improve health and production under different conditions and practices.