Supervisor: Dr Silvia Liverani
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 invasives and (2) areas in need of protection in the face of climate change, changing land use (especially agriculture) and pollution.
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.
Further information: How to apply Entry requirements Fees and funding