Estimating marine mammal abundance and distribution from passive acoustic and biotelemetry data


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. The student will, firstly, apply and further develop machine-learning techniques to identify individual species. Secondly, the student will leverage large marine mammal tracking datasets, as well as abundance and distribution predictions, to compare and integrate tracking, distribution and abundance data with PAM data.


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.


The student 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.


The student will apply and further develop existing software tools for analysing large acoustic datasets for individual species. After training, the student will 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.


The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and the student will be registered at the University of Southampton and hosted at the School of Ocean and Earth Science at the National Oceanography Centre Southampton. Specific training will include attendance at courses in the Faculty of Engineering and Physical Sciences on marine acoustics, signal processing and/or machine learning. In addition, there are a wide range of masters level modules available in Oceanography at NOCS, and it would be expected that some of these modules would be taken. The PhD student will benefit from a supervisory team which has world-leading expertise in marine mammals, ocean acoustics, signal processing and autonomous systems, as well as Southampton’s membership of the Turing Institute (a national focus of data science and artificial intelligence).


Eligibility & Funding Details: 

Please see for details.


Background Reading: 
  1. White, E.L., White, P.R., Bull, J.M., Risch, D., Beck, S., and Edwards, E.W.J., 2022. More than a whistle: automated detection of marine sound sources with a convolutional neural network. Frontiers in Marine Science,
  2. Reisinger RR, Friedlaender AS, Zerbini AN, Palacios DM, Andrews-Goff V, Dalla Rosa L, Double M, Findlay K, Garrigue C, How J, Jenner C, Jenner M-N, Mate B, Rosenbaum HC, Seakamela SM, Constantine R (2021)
    Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales. Remote Sensing 13(11): 2074.
  3. Hindell MA, Reisinger RR, et al. (2020) Tracking of marine predators to protect Southern Ocean ecosystems. Nature 580: 87–92