Suppervisors: Andrew Brierley, Paul Fernandes

Project description:

Fish provide important protein for human consumption, either directly (humans eat fish) or indirectly via animal feed (humans eat other animals fed on fish meal). Long-term sustainable fish harvest is therefore a major contributor to human food security and agriculture (which includes the cultivation and breeding of animals). This PhD will develop acoustic techniques to improve acoustic identification and assessment of fish in Lake Victoria, Africa’s largest lake. About 35 million people in the Lake Victoria basin depend on fish, fishing and fish products (Mgaya and Mahongo, 2017). Although the introduced Nile Perch is well known (generally and scientifically), this species is not so important for local, African consumption (it is exported as a luxury food for to the developed world). In a local nutritional context the Silver Cyprinid (Rastrineobola argentea; known locally as ‘dagaa’) is much more important. Dagaa is a schooling fish. We have an ongoing Global Challenges Research Fund project on Lake Victoria. In this PhD, we will develop automated techniques to extract dagaa schools from over 10 years of acoustic survey data and progress towards improved biomass estimates for the species. The school detection approach will be based on previous work (by Brierley) on automated detection of Antarctic krill and (by Fernandes) on random forest classification to discriminate various species of marine schooling fish. We will also use image analysis techniques to improve the signal processing element of Nile Perch detection, which although not as vital for direct food security, provides much needed local income for poverty alleviation.

This PhD will provide training in collection and analysis of acoustic survey data (including fieldwork on Lake Victoria).  It will also provide essential numerical skills in the latest machine learning techniques in object detection and classification. The project provides unique interdisciplinary studies involving mathematical algorithm development, signal processing, and biological analyses, working in the developing world to enhance food security.

References:

Fernandes, P.G. (2009). Classification trees for species identification of fish-school echotraces. ICES J Mar Sci 66, 1073-1080.

Mgaya, Y.D. and Mahongo, S.B. (Eds.) (2017). Lake Victoria Fisheries Resources. Springer.

To apply for this project, please go to this link.