Monitoring health and unlocking social behaviour in bottlenose dolphins with interdisciplinary next generation technology

Monitoring health and social behaviour in cetacean populations is required for population management and quantification of human impacts. Measures of individual fitness, survival, reproductive success, and sociality can have far-reaching implications for wildlife management and conservation, as populations adapt, or not, to human disturbance. Quantifying individual interactions is the foundation of social behaviour and cetaceans arguably demonstrate some of the most complex social systems in the mammalian world. However, the nature of social relationships in cetaceans remains poorly studied. Cetaceans provide unique research challenges that can constrain data collection and prevent multimodal inference. Recent developments in marine robotics, artificial intelligence (AI) and bioacoustics open opportunities for a technology-driven approach for conservation and behavioural research. AI-based techniques employing machine learning to analyse unoccupied aerial systems (UAS)-captured footage, and acoustic data need integration into tools to extract behavioural patterns and allow application to conservation research. The Scottish bottlenose dolphin project is one of the longest running individual-based studies of dolphins in the world, with multi decade sighting histories and life history data. This population has high societal importance, with core habitat impacted by coastal developments and in key areas for UK renewable energy. This project is an opportunity to integrate new generation technologies and contribute vital population and individual level information for conservation management and compliance monitoring for UK renewables.

The student will collect acoustic, behavioural and UAS data during the annual summer field campaigns run by the Lighthouse Field Station based in Cromarty, Scotland. They will use a modified multi-rotor UAS, fitted with a laser altimeter to collect high-resolution still and video imagery of dolphin groups to verify individual identity information from known bottlenose dolphins within the population, and deploy hydrophones to record underwater acoustics. The student will explore dolphins’ fine-scale spatial interactions and individual movement trajectories and their link to acoustics, as well as morphometric and demographic data. The student will utilize and integrate a suite of AI methodologies to test hypotheses relating to the influence of social relationships and individual identity on group movement and acoustics in dolphins. 

This PhD project will integrate technology-driven data collection across bioacoustics, marine robotics and artificial intelligence to advance animal behaviour research. The project has four main research objectives: 

We invite highly motivated candidates with a first or upper second-class honours degree and preferably a relevant MSc in either an appropriate biological, behavioural, computer science or other highly numerate/quantitative field. We recognise candidates are unlikely to be both biologically and computer science trained, so the successful candidate must demonstrate capacity to learn new skills and adapt their knowledge to this multi-disciplinary project. Strong quantitative and numeracy skills are essential. Acoustic analysis skills, competency using a UAS and boat-based field experience in marine mammal science are all desirable. This project has field and desk-based components, so the candidate must be prepared to spend their time between Plymouth and Cromarty, Scotland. 

As part of the doctoral teaching associate requirements for this position, the successful applicant will be expected to assist in demonstrations and processing sessions related to sidescan and multibeam hydrographic surveys and CTD and ADCP oceanographic surveys. Instruction and guidance will be provided but sufficient background in numeracy to facilitate that training will be assumed.

This project will provide the student with multi-disciplinary skills development and training across bioacoustics, behavioural ecology and artificial intelligence. They will complete small boat work and field data collection at a world leading field station enhancing their data collection skills and providing benefits of interaction across two institutions. The student will also benefit from access to the Scottish bottlenose dolphin project longitudinal dataset to inform the individual level analysis that underpins this project, as well as a highly skilled field team to facilitate data collection and individual training. They will receive dedicated training in UAS operation and maintenance and depending on background may receive training in ecology, bioacoustics, artificial intelligence applications and R and Matlab programming.