Marine habitats provide a wealth of ecosystem services to the marine and terrestrial environments and to people. At the same time, these habitats are under increasing pressure from human activities in the coastal zone and climate change impacts, leading to disturbance and degradation. Recognition of the ecosystem services that marine habitats provide has highlighted the need for conservation and restoration, however, such initiatives necessitate accurate mapping and monitoring of marine habitats at increasingly large spatial scales.
Understanding what types of marine habitats exist in different locations and unravelling the underlying drivers of their distributions have great potential value for the overall protection and evaluation of ecosystem services. Habitat type drives biological community formation and ecosystem dynamics, with implications for biodiversity and carbon storage. Yet mapping marine habitats using traditional methods (e.g. in-situ observations by divers) is a costly and weather-dependent activity, and is further hindered by the large spatial scales involved. This highlights the need to develop new approaches and harness the increasing availability of sophisticated datasets and ability of machine learning techniques.
This PhD project will utilise novel data for remote mapping of marine habitats in Southwest England, with opportunities to extend the approach that is developed more widely around the UK. Data sources to explore will include sonar, satellite/aerial imagery, acoustics, and others to be determined during the project. Sensor data will be combined with existing records of marine habitats to create training data for development of a machine learning tool to detect different habitat types. The project will first focus on the mapping of seagrass beds, which are known to provide invaluable ecosystem services but are undergoing worldwide loss and degradation. Once machine learning techniques and related workflows have been developed and tested in their ability to identify seagrass beds, the approach will be applied to other marine habitats (e.g. kelp forests, reef systems). In addition to habitat mapping, the project will investigate key factors that constrain the development of habitats in order to identify and prioritise areas for habitat rehabilitation.
The PhD student will work with an interdisciplinary research team involving geographers and data scientists from the University of Exeter and conservationists from lead partner, the Devon Wildlife Trust and the wider South West Wildlife Trust organisations. The student will join the CDT Environmental Intelligence at University of Exeter where they will receive training in relevant methods before conducting their research project. The project will make a critical contribution to the management of coastal systems by identifying areas of valuable marine habitats for protection and potential locations for restoration.
About the UKRI Centre for Doctoral Training in Environmental Intelligence
Our changing environment presents a series of inter-related challenges that will affect everyone’s future health, safety and prosperity. Environmental Intelligence (EI) is the integration of environmental and sustainability research with data science, artificial intelligence and cutting-edge digital technologies to provide the meaningful insight to address these challenges and mitigate the effects of environmental change. One of the 16 UKRI AI CDTs launched in 2019, the CDT in Environmental Intelligence provides an interdisciplinary training programme for students covering the range of skills required to become a leader in EI:
• the computational skills required to analyse data from a wide variety of sources;
• expertise in environmental challenges;
• an understanding of the governance, ethics and the potential societal impacts of collecting, mining, sharing and interpreting data, together with the ability to communicate and engage with a diverse range of stakeholders.
The CDT cohort works and learns together, bringing knowledge, skills, and interests from a range of academic disciplines relevant to EI. CDT students undertake training and professional development as a cohort, and regularly participate in seminars, symposia, and partner engagement activities including the annual CDT Environmental Intelligence Grand Challenge. As part of the research community at the University of Exeter, CDT students benefit from networking with colleagues in the Institute for Data Science and Artificial Intelligence; the Global Systems Institute; and the Environment and Sustainability Institute.
Eligabhttps://www.exeter.ac.uk/pg-research/apply/english/ility:
This award provides annual funding to cover Home tuition fees and a tax-free stipend. For students who pay Home tuition fees the award will cover the tuition fees in full, plus at least £17,668 per year tax-free stipend. Students who pay international tuition fees are eligible to apply, but should note that the award will not provide the international element of the tuition fees (approx £15,000 per annum).
International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD.
The conditions for eligibility of home fees status are complex and you will need to seek advice if you have moved to or from the UK (or Republic of Ireland) within the past 3 years or have applied for settled status under the EU Settlement Scheme.
Entry requirements
Applicants for this studentship must have
• obtained prior to the start of the PhD, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology, i.e. environmental, geographical, mathematical, or computer science study programme.
• a keen interest in environmental research
• some understanding of machine vision principles and/or spatial technologies
• sound numerical and computational experience
• If English is not your first language you will need to meet the required level (Profile A) as per our guidance at https://www.exeter.ac.uk/pg-research/apply/english/
Desirables:
• some experience in coding languages (e.g. Python)
For further information and to apply please use this link: Award details | Funding and scholarships for students | University of Exeter
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