Title: Changes in the distribution and abundance of marine top-predators in the North Sea associated with present and future wind farm developments
- Location: School of Ocean Sciences, Bangor University, UK
- Project ID: F009516
- Application deadline: 26 July 2021 18:00 (BST)
- Source of Funding: INSITE programme
- Duration of PhD: 1 October 2021 – 31 March 2025
- Annual stipend: £16,178
- Supervisors: Prof. Simon Neill and Dr James Waggitt
We are pleased to announce an exciting opportunity for a fully-funded PhD studentship on the distribution of marine top predators in the North Sea. The studentship is based in the School of Ocean Sciences, Bangor University, UK.
Recent decades have witnessed considerable changes in the distribution and abundance of marine top-predators in the North Sea. Although these changes can be broadly explained by impacts of increasing temperature on prey communities, local-scale trends suggest other processes are important. The development of large-scale wind farms and associated changes in physical conditions adds another dimension to these scenarios, with potential changes to near- and far-field habitats. The manner and magnitude of these changes depend upon the scale, density and siting of the wind farms. In addition, fundamental differences in the design of wind turbines, including the possibility of future floating wind turbines in deeper waters, could also affect their impact.
This project will make use of databases which document spatial/temporal variations in the occurrence of top-predators, fish and plankton, in combination with high-resolution modelling of oceanographic processes to (1) identify critical habitats for top-predator communities in the North Sea, (2) explain changes in abundance and distributions over recent decades, and (3) use this understanding to predict changes following installations of arrays of wind turbines.
Through the project, you will become an expert in parameterizing and running state-of-the-art 3D models on supercomputers, including coupling of multiple physical and biological processes. You will become proficient in statistical analysis, applied to model validation and the analysis of disparate datasets. You will be trained in advanced ecological analyses in R, including Generalized Linear Models (GLM), Generalized Additive Models (GAM), Mixed-Effect Models and Generalized Estimating Equations (GEE). You will also gain experience in the processing of large datasets in R Statistics, acquainting yourself with netCDF and ASCII files. Being based at the School of Ocean Sciences (Bangor University) you will have ample opportunities to participate in the collection of biological and oceanographic data from research vessels, providing an invaluable combination of practical and analytical skills.
Entry requirements: Minimum UK honours degree at 2:1 level or equivalent in a relevant science, engineering or mathematical discipline.
For informal discussions about the studentship please contact: Prof. Simon Neill, School of Ocean Sciences, Bangor University, email: firstname.lastname@example.org