We invite applications for a fully-funded, full time PhD studentship to work as part of a multi-disciplinary team (including behavioural biologists and physiologists) on a project funded by UKRI (BBSRC) that is aiming to understand and predict the effects of the thermo-nutritional developmental environment upon the life history of two key UK wild bee species, and the consequent implications for pollination services. The aim of the PhD studentship will be to develop computational models that will form a key part of the overall project, underpinning our predictions of how environmental variables affect the life history of two bee species.
Bees are vital for ecosystem stability and global food security – providing pollination services worth hundreds of billions of pounds annually. The UK has ~245 species of wild bees, collectively performing more pollination than managed honeybees and bumblebees. All animals need a balance of key nutrients, such as protein, carbohydrate and fat, in their diets for growth, maintenance and reproduction. However, different environmental conditions, such as temperature, may necessitate different balances. Temperature affects animals’ metabolic rate, physiology, digestion, and nutrient assimilation. For bees, this is an unassessed and possibly serious threat both to their health and their pollination services upon which we depend for our food security. Bees' health depends on access to a mix of nutrition appropriate for the conditions - but the climate is heating up, so this ideal mix may change. Crucially, if this affects bees’ flower choices, then the pollination services bees provide today may not be the same at higher temperatures.
To address these issues and achieve the central aim of the project, we will study two wild pollinator species representing two distinct contrasting lifestyles of UK bees - social-nesting buff-tailed bumblebees (Bombus terrestris), and solitary-nesting red mason bees (Osmia bicornis). Both are commercially important pollinators, but they have important differences in life history that may result in different responses to nutrition and temperature.
The successful candidate will have a background in a quantitative or computational scientific discipline (e.g. Informatics, Mathematics, Physics, Engineering). They will use these skills to apply cutting-edge modelling techniques to create and parameterise computational models using a combination of existing experimental data gathered by the project team and previous literature. The models will be used to: (i) test hypotheses relating bee development, health and reproduction to achieve changes in practice (such as the sowing of wildflower strips of different nutritional composition in the context of predicted temperature shifts); and (ii) augment existing published models of bee reproduction and effectiveness as pollinators across conditions, landscapes and climates, making them nutritionally and thermally explicit.
The PGR will use, among others, Dynamic Energy Budget (DEB) models that permit modelling of multiple physiological parameters across bees’ life histories. Such models use reaction kinetics and physico-chemical processes to dynamically estimate the uptake, allocation and usage of nutrients into and out of different functional pools (structure, reserve, maturity, reproduction) across organisms' lifetimes [1]. The models will be implemented in Python, R or MATLAB.
For informal inquiries, please contact
For informal inquiries, please contact [Email Address Removed].
About the research cluster / about the research environment
The PGR will be based in the Department of Biology in the School of Natural Sciences at the University of Hull. The University of Hull is in the Times Higher Education's top global 100 for research impact and is one of the highest climbers in the REF 2022, ranking 55th. The research will be based at the Department of Natural Sciences, which has multiple research groups focussed on monitoring and management of environmental change at the molecular, metabolic, individual, landscape, social and commercial levels. The two co-supervisors will be based in the School of Life Sciences at the University of Sussex, which is ranked in the top 20 biological sciences departments in the UK (REF 2022) and includes >10 highly interactive research groups focussed on insect behaviour, conservation, evolution and ecology.
The successful candidate will capitalise on this opportunity to synthesize the research interests of these research groups and create collaborative links between institutions. The candidate will be integrated into both institutions and will benefit from the infrastructure and connections at both universities.
Funding
The successful applicant will receive a fee waiver and a maintenance grant / stipend for four years (full-time), which covers the research period of the PhD and weekly teaching commitments, spread over four years. The fee waiver for 23/24 is £ 4,712 (Home fee) and the maintenance grant is £18622 per annum. This rises each year in line with the UKRI’s recommended stipend allowance.
Submission of thesis
Submission of your final thesis is expected within three years and three months from the start of your PhD scholarship.
Eligibility and entry requirements
Applicants should have a minimum 2:1 degree in biological, physical, mathematical or computational sciences, ideally with a demonstrable interest in mathematical and/or modelling approaches to biological questions. A taught MSc or Masters by Research in computational modelling, applied mathematics or related discipline would be an advantage.
For more details on our entry requirements please visit the University of Hull’s postgraduate admissions webpage. This scholarship is available for full-time study.
International applicants
This opportunity comes with a Home fee waiver only, which will not cover the full International fee. You will therefore need to pay the difference between the Home fee and the International fee and will need to provide evidence that you have sufficient funds to cover this as no additional funding is available.
How to apply
Closing date for applications - 13th October 2023
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