Supervisory Team: Paul White, Jonathan Bull and Ryan Reisinger
Project description
Ecosystem function weakening due to reduction in top predator numbers is a first order global problem. In the oceans anthropogenic activities adversely affect marine mammals, with 25% of species being threatened. Determining their spatiotemporal distribution and abundance is central to understanding ecosystem health. The aim of this studentship is to combine Passive Acoustic Monitoring (PAM) and satellite-linked tracking (biotelemetry) to determine marine mammal abundance and distribution. Determining a reliable distribution of animals from these two contrasting techniques will require careful comparison, data integration and insight, as PAM techniques require identification of individual species from their call types, while in biotelemetry specific animals are tracked.
Marine autonomous vehicles are effective in sensing and understanding the oceans and can be equipped with PAM devices that can record a large frequency bandwidth facilitating a high-fidelity and complete record of the marine soundscape. Interrogating the vast datasets that are recorded by fleets of autonomous data is a current challenge.
This project will determine the distribution and abundances of marine mammals using data from animals tracked with satellite-linked tags, and animal vocalisations recorded on acoustic sensors attached to fixed moorings and autonomous underwater vehicles.
You will analyse animal tracking data from the Argos system using existing software implementations of Hidden Markov Models to infer locations at regular time intervals, while accounting for uncertainty in the location estimates. These regularized tracking data will be used to develop a variety of density surface models to estimate the abundance and distribution of marine mammals.
You will apply and further develop existing software tools for analysing large acoustic datasets for individual species. You will develop and apply machine learning techniques to enable discrimination of vocalisations from individual species using data from acoustic recorders mounted on autonomous systems and fixed buoys, with data available from both the Atlantic and Southern Ocean. These data will be compared to distribution and abundance model estimates derived from satellite-linked tracking. The student will investigate and develop methods for fusing tracking data and acoustic data for improved distribution and abundance estimation.
You will need to have well-developed computer skills, preferably with experience in Python or MATLAB.
If you wish to discuss any details of the project informally, please contact Paul White, Signal Processing and Hearing Research Group, Email: prw@soton.ac.uk Tel: +44 (0) 2380 592274
Entry Requirements
A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).
Closing date: applications should be received no later than 01 April 2023 for standard admissions, but later applications may be considered depending on the funds remaining in place.
Funding: For UK students, Tuition Fees and a stipend of £17,668 tax-free per annum for up to 3.5 years.
How To Apply
Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk). Select programme type (Research), 2023/24, Faculty of Physical Sciences and Engineering, next page select “PhD Engineering & Environment (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Paul White