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Postgraduate research project

Combining passive acoustic and biotelemetry data for studying marine mammals

Funding
Competition funded View fees and funding
Type of degree
Doctor of Philosophy
Entry requirements
2:1 honours degree View full entry requirements
Faculty graduate school
Faculty of Engineering and Physical Sciences
Closing date

About the project

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. 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. These devices 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 use 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. You will investigate and develop methods for fusing tracking data and acoustic data for improved distribution and abundance estimation.