The QMEE CDT Project proposal database

Welcome to the QMEE CDT Project proposal database. This is a live list of projects proposals put forward by PIs across the CDT partner institutions

PIs/Supervisors will continue to add projects to this list over the next few months, so do keep checking back! You can search the projects using the box below: simply enter some text and press Search to do a text search across all the database fields. If you want to search more finely, the search tool also allows you to search on particular details of the project descriptions: you will see these finer search options appear if you click on the search box.

Click on the view button next to a project to get the full proposal description. If you want to download project details, either for all projects, or for a subset you have searched for, then click on the 'Download details' button.

To find a particular PI's email or look up other PI details, use the menu at the top of this page (PIs tab).

Bees and plants in the climate crisis
Interactions between plants and the bees that pollinate them are dynamic and influenced by the weather. Droughts limit the amount of nectar in flowers and force bees to visit more flowers to harvest the same amount of energy, while high temperatures damage pollen in flowers and increase plants’ reliance on cross pollination by bees. Climate factors therefore impact the reproductive success of bees, with knock-on impacts for the fitness of plants. Bees collect energy and other resources by flying between flowers and harvesting their resources. Flight draws on bee energy reserves, but these are replenished by feeding. Foraging patterns can therefore be modelled in a straightforward way following established principles of behavioural and physiological ecology (e.g. Sibly et al. 2013 doi:10.1111/2041-210x.12002). However, not all flowers are equal. Plants vary in amounts of rewards provided for bees, in ability to tolerate water shortages and high temperatures, and as a result of growing in damper soils or more shady areas (micro-climates). Alongside direct fitness consequences, drought likely has indirect fitness consequences for plants by altering the foraging choices that bees make and the movements of pollen between plants (e.g. Bishop et al. 2017 doi: 10.1093/jxb/erw430). This PhD project will focus on i) developing new mathematical models of within-patch floral foraging, and ii) adapting landscape-scale models of colonial nesting bee species which have been used in the past to model impacts of pesticides and habitat loss on bee colony health (BEEHAVE; Becher et al.). Modelling at these two spatial scales will help us to understand how climate factors change foraging choices and fitness of bees, and the knock-on impacts of this for geneflow and fitness within and between flower patches. The student will conduct a range of experimental research to inform their model development. Initially, this will involve simple experiments to quantify how drought changes the nectar and pollen provision of plants – this will be used to modify the quality of floral patches within the landscape-scale models and corresponding bee colony fitness. Later in the project, the student will undertake complex experiments where bees are allowed to forage on plants of varying drought status (achieved either by modifying the habitat (water provision) or the genotype (genetic drought tolerance)) – this will allow testing of the model of bee foraging choices at a small scale. Testing of predictions for plant fitness will be achieved by measuring seed production and the paternity of each seed with genetic analyses. The project will provide a novel insight into the impacts of an increasingly stressful climate on plant-pollinator interactions, and the mathematical models will have important onward applications in pollinator and plant conservation.
Jacob Bishop
Richard Sibly
Quantitative data analysis, Ecological observations / data collection
Richard Sibly
Quantifying the components of the bee energy budget using real data, use of data for model construction and validation, expertise in GLMs and LMMs, development of Agent Based Models (ABMs) of bee foraging in relation to plant distribution and weather, and evaluation of ABMs using Approximate Bayesian Computation.
The quantitative aspect of the project will address important limitations in the existing model of bumblebees (BumblebeeHAVE, Becher et al.), adding patch-scale foraging choices (via ABM) and accounting for differences in floral resources that result from different climate conditions. In addition we will incorporate important feedbacks for geneflow and fitness of plant communities.
The project will address problems in foraging theory and energy budgeting using agent based modelling to investigate the population consequences of stressors that impact individuals. The investigated stressors affect plants as well as bees so we will also look at the effects of the stressors on plant pollinator interactions and plant mating systems.
This new work will enable risk assessment of bumblebee and entomophilous plant population viability under future climate conditions. Our fine scale models will allow prediction of geneflow in plant populations under such conditions.
The model outputs may be used to advise fruit crop growers about the costs/benefits of for example, supplementing their fields with managed pollinators, or the use of irrigation in non-crop foraging areas. The understanding of impacts of stress for geneflow in plant populations could guide new hybrid breeding strategies of insect pollinated crops.
This project brings together quantitative expertise in agent based modelling (Sibly) with empirical expertise in plant-pollinator interactions (Bishop). By combining our experimental and quantitative approaches we have developed a research project that addresses a key gap in understanding of how plant-pollinator interactions and the resulting fitness of both will be affected by climate change.
Climate and climate change, Behavioural ecology, Environmental physiology
The project offers training in controlled environment experimentation, entomology, statistical, mathematical and analytical skills, and programming skills in open-source languages including R, and NetLogo for Individual-Based Modelling. Approximate Bayesian Computation will be used to evaluate the IBM.
University of Reading School of Biological Sciences and School of Agriculture, Policy and Development
No
2019-05-30 17:14:41