Bayesian spatial modelling for biodiversity
- Supervisor: Dr Silvia Liverani (Maths)
- Funding: Queen Mary Principal's Studentship
- Deadline: 21st February 2022
The following fully-funded PhD studentship is available with an expected start date of September 2022. The studentship will be held in the School of Mathematical Sciences.
Project description
Many models assume that observations are obtained independently of each other. However, distance between observations can be a source of correlation, which needs to be accounted for in any models. For example, pollution has a spatial smooth pattern and measurements close in space are likely to be very similar. Spatial models will therefore have to consider any spatial autocorrelation in datasets in order to separate the general trend (usually depending on some covariates) from the purely spatial random variation.
This project will focus on developing and applying Bayesian spatial and spatio-temporal modelling techniques to predict (1) plant species that are the cause for concern, for example species at risk of extinction or of being invasive and (2) areas in need of protection in the face of climate change, changing land use (especially agriculture) and pollution. The pollutants of interest are nitrogen- and phosphate-based fertilizers. We will leverage spatial distribution data for the entire British flora, studying changing trends in distribution and land use over 60 years and including a range of different measures of genomic diversity, such as a species genome size and its polyploid status. We will also exploit climate and soil data to uncover the role of biological and abiotic factors in predicting plant distribution over landscapes scales and hence those area that are particularly vulnerable to land use and climate change scenarios. The research will be undertaken in collaboration with Dr Ilia Leitch, Senior Research Leader at the Royal Botanical Gardens, Kew.
One statistical challenge that arises in this study is that the data available are at different resolutions. Advanced methods are required to model misaligned spatial and spatio-temporal data. We will leverage recent work by Dr Silvia Liverani on Bayesian methods for misaligned areal data, and extend them to suit meet the needs of this research challenge in the study and understanding of biodiversity.
Funding
This studentship is funded by QMUL. It will cover tuition fees, and provide an annual tax-free maintenance allowance for 3 years at the Research Council rate (£17,609 in 2021/22).
The project is open to UK and international students. The higher fees for international students (including EU) may be covered for up to 2 candidates applying for the Queen Mary Principal's PhD Studentships: Environment, Biodiversity and Genomics.
Eligibility and applying
Applications are invited from outstanding candidates with or expecting to receive a first or upper-second class honours degree in Statistics. An appetite for coding and mathematical modelling to solve problems in evolutionary genetics is more important than extensive experience
Applicants from outside of the UK are required to provide evidence of their English language ability. Please see our English language requirements page for details.
Informal enquiries about the project can be sent to Dr Silvia Liverani (s.liverani@qmul.ac.uk). Formal applications must be submitted through our online form by the stated deadline including a CV, personal statement and qualifications.