UK – Phytoplankton form the base of the marine food web. Through their primary production they are crucial to carbon cycling. However, some species form “harmful algal blooms” (HABs) which have had a severe impact on the finfish and shellfish aquaculture industries in Scotland over the last decades.
The aquaculture industry and associated policy makers require a rapid early warning of the development of HABs and a better understanding of their response to environmental forcing in a changing climate. This will help make informed management decisions such as deploying protective tarpaulins, reducing feeding, moving cages (fish farming), and early or delayed harvest or increased end product testing (shellfish farming) and could help protect human health (typically from HAB generated toxins vectored to humans by shellfish) and minimise mortalities of farmed fish as a consequence of other HAB genera.
A solution to the problem is the Imaging FlowCytobot (IFCB) https://mclanelabs.com/imaging-flowcytobot/. This is an in-situ automated submersible imaging flow cytometer, that also generates images of phytoplankton in-flow. The IFCB can track the progression of phytoplankton cycles or HAB events. Data collection is achieved through a novel combination of flow cytometric laser based and video technology to capture high resolution images of suspended particles. Collected images are processed using automated image classification software.
SAMS has been successful in obtaining funding for the 1st IFCB in the UK. This instrument will provide a step change in the capability of UK environmental science to generate data to monitor and understand phytoplankton dynamics. The student will therefore become the UK expert in this innovative technology.
The aim of the studentship is to achieve the 1st UK installation of an IFCB producing high resolution phytoplankton data for the early warning of HABs and interpretation of influence of environmental drivers on HAB developments.
A collaboration between SAMS, the NAFC Marine Centre and Marine Scotland Science with funding from Scottish Government and the Data Lab, this project will provide the capability to classify, display and analyse IFCB data. The studentship will address four challenges:
1) Achieving the 1st routine UK operation of an IFCB
2) IFCB image classification to discriminate and enumerate different phytoplankton species
3) New methods to display HAB data for easy interpretation by stakeholders
4) Application of data analysis approaches to improve risk assessment and ecological understanding
To study the relationship between environmental factors and phytoplankton abundance, we will utilise various modelling approaches including traditional ones like Random Forest to deep learning based approaches such as Long short-term memory (LTSM) that may be more capable of representing the complex factors that promote HAB formation than deterministic approaches.
The studentship requires a candidate that is interested in both the practical and data aspects of environmental science. Previous experience in image processing, high-level programming languages such as Matlab or Python and machine learning may be beneficial. An interest in phytoplankton taxonomy and a solutions mentality to instrument operation are also required.
In Year 1, the student will receive initial training (1-2 months) at SAMS before they and the instrument relocate to NAFC. Subsequent year 1 activities will relate to 1) the deployment of the instrument at NAFC during the summer phytoplankton growth season, 2) progressing the development of phytoplankton classifier and 3) initial development of the on-line portal.
Interaction with supervisors at SAMS and Marine Scotland Science will be through regular video-conferencing based meetings and, covid restrictions willing, 6 monthly face to face meetings. Collaboration with the wider IFCB user community will be on a dedicated GitHub hosted by Woods Hole Oceanographic Institute.
In Year 2 and 3, the instrument will continue to be deployed at NAFC (or adjacent aquaculture site).
The IFCB data will be available in real time over our internal IT network. Hence, depending on the student’s preference and the IFCB maintenance requirements, the student may be located at NAFC or SAMS in years 2 and 3.
The student will combine high resolution IFCB phytoplankton data with environmental data and novel deep learning modelling approaches offering the potential to improve our understanding of the factors driving HABs and hence improve these forecasts.
Please contact Professor Keith Davidson for further information (Keith.Davidson@sams.ac.uk)
Applications Closing date: Wednesday 2 December 2020
Interview date: Wednesday 16 December 2020
Starts: Spring 2021
The 3.5 year studentship covers:
• Tuition fees each year (for 2020/21 this is currently £4,407 for full-time study)
• A maintenance grant each of around £15,000 per annum (for full-time study)
• Funding for research training
Applicants should normally have, or be studying for:
• A postgraduate Master’s degree from a degree-awarding body recognised by the UK government, or equivalent, or
• A first or upper second class honours degree from a degree awarding body recognised by the UK government, or equivalent.
Dead line: Wednesday, December 02, 2020