PhD studentships

Bouncing back from macroalgal-dominated reefs: microbial and chemical effects of algal removal and coral transplanting.

Supervisors:
Dr Miriam Reverter, University of Plymouth
Dr Mahasweta Saha, Plymouth Marine Laboratory
Dr Andy Foggo, University of Plymouth, School of Biological and Marine Sciences

Climate change is causing major ecological shifts, with important consequences for ecosystem functioning. In coral reefs, coral-algal transitions are the most well-known shift. Following algal increases, an array of mechanisms that reinforce algal dominance and prevent coral recovery are established. For example, algal chemicals can promote changes in microbial communities, negatively affecting corals. Algae can damage corals upon contact through the presence of chemical defences or by transferring opportunistic bacterial pathogens. To date, understanding of these mechanisms, their reversibility, and their effects on key reef functions remains limited. The objective of this project is to investigate the microbial and chemical mechanisms that reinforce algal-dominated reefs and to evaluate if algal removal and coral transplantation can reverse these mechanisms to help recover coral-dominated reefs.

Building a digital twin navigating autonomous underwater vehicles to monitor water quality

Lead Institution: Plymouth Marine Laboratory
Lead Supervisor: Jozef Skakala, Plymouth Marine Laboratory, Modelling group
Co-Supervisor: Prathyush P Menon, University of Exeter, Faculty of Environment, Science and Economy
Co-Supervisor: Juliane Wihsgott, Plymouth Marine Laboratory, Marine biogeochemistry group
Co-Supervisor: David Ford, Met Office, Exeter

We propose for the student to develop a proof-of-concept DT to demonstrate that multiple AUVs could be successfully navigated to investigate oxygen depletion/minima zones using their own measurements (of temperature, salinity, chlorophyll and oxygen itself) as well as Earth Observation (EO) and model data. The initial plan, which we are happy to later adapt to the student's initiatives, needs, and interests, is that the student will run virtual DT experiments using a state-of-the-art physics-biogeochemistry ocean model (NEMO-FABM-ERSEM), acting as the "real-world ocean" in which AUVs take "samples". Virtual EO data will be similarly extracted from the model outputs and perturbed by noise to represent observational uncertainty, providing an idealized framework in which the true ocean state and all model and observational errors are known, allowing the effectiveness of the developed ML/AI algorithms to be accurately assessed. Building on a recent work of Skakala et al (2023), the student will develop ML model to forecast DO values from the virtual glider measurements and EO data. The ML model will then be used together with path-planning algorithms, designed by the student, to optimize the trajectory and sampling strategy of multiple gliders. The work will demonstrate the feasibility of the DT AUVs control for tracking hypoxia and provide a framework for real-world applications. The work will further investigate/advise what variables need to be measured, as well as their locations and sampling resolution, for a successful future hypoxia tracking mission.

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The Transport & Cycling of Terrigenous Carbon in UK and Falkland Island Coastal Waters

Lead Institution: Plymouth Marine Laboratory
Lead Supervisor: Andy Rees, Plymouth Marine Laboratory
Co-Supervisor: Dan Mayor, University of Exeter, Biosciences
Co-Supervisor: Chris Evans, UK Centre for Ecology and Hydrology, Bangor
Co-Supervisor: Paul Brickle, South Atlantic Environmental Research Institute
Co-Supervisor: Vas Kitidis, Plymouth Marine Laboratory

During this project, you will investigate processes that transform carbon transported between land and the ocean within several contrasting environments in order to better understand the fate and impact in receiving waters and overlying atmosphere. There will be opportunities to join existing research teams at the host organizations to address high-level questions related to carbon transport and transformation.

1. What mechanisms control CO₂ and CH₄ release from an agriculturally dominated estuary (Tamar - SW England)
2. How do the carbon characteristics differ between estuaries and coastal waters associated with drained and re-wetted peatlands (East Anglia)
3. How does remineralization of peat deposited on the sea-bed impact the chemistry of benthic sediments and overlying waters (Falkland Islands)

Human activities on land have resulted in the elevated release of material between land and ocean, and this project will work alongside areas of impacted land and restoration attempts. Each of the areas highlighted above will require the student to work between rivers and the coastal zone to collect samples and deploy sophisticated analytical instrumentation to study dissolved and particulate carbon, dissolved oxygen, and methane and CO₂ concentrations and fluxes.

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Microscopic ocean life viewed through a new lens

Lead Institution: Plymouth Marine Laboratory (PML)
Lead Supervisor: Dr James Clark, PML, Marine Systems Modelling
Co-Supervisor: Dr Sareh Rowlands, University of Exeter, Computer Science
Co-Supervisor: Dr Sophie Pitois, Cefas, Ecology and Plankton Science
Co-Supervisor: Elaine Fileman, PML, Marine Ecology and Biodiversity
Co-Supervisor: Claire Widdicombe, PML, Marine Ecology and Biodiversity
Co-Supervisor: Robert Blackwell, Cefas, Data science and statistics

The project can be steered in several directions, and the successful student will have the opportunity to shape the project around their interests. Example research questions include the variability of plankton abundances on hourly-daily timescales, spatial scales governing patchiness in plankton communities, and plankton populations' responses to short- and long-term perturbations. The student may also decide to research how these new data types can be integrated into existing plankton survey and monitoring programs, which rely on more traditional measuring techniques.
The following gives an example of a set of objectives around which a project could be built:
i) Collect existing and new data on plankton abundances using the Pi-10 and IFCB cameras.
ii) Assess the efficacy of existing machine learning algorithms for classifying planktonic organisms within image data collected by the Pi-10 and IFCB cameras.
iii) Quantify and investigate sub-daily changes in plankton community composition at Station L4 in the Western English Channel using data from the IFCB and Pi-10 camera systems.
iv) Quantify and investigate short (~m) spatial scale changes in plankton community composition in Pi-10 image data collected aboard the RV Cefas Endeavour.
It is anticipated the student will work directly with the IFCB (PML) and Pi-10 (PML, Cefas) camera systems, and will take part in research cruises organised by both PML and Cefas. They will help to deploy, operate and service the three cameras; and use the data to study plankton communities. They will work with Machine Learning software to classify plankton within the images and use time series analysis and statistical approaches to tease apart patterns within the data and drivers of variability.

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Identifying the chemical signals needed to revive dormant bacteria

Lead Supervisor
Dr Sariqa Wagley, NERC Independant Research Fellow, University of Exeter

Additional Supervisors
Dr Eunice Lo, Research Fellow in Climate Change & Health, University of Bristol
Dr Mahasweta Saha, Senior Scientist, Plymouth Marine Laboratory

When bacteria encounter extreme or unfavourable environmental conditions, they enter a state of dormancy to protect themselves, however, they can re-awaken when favourable environmental conditions return. These dormant cells can act as a reservoir of disease in the environment and are a concern for human health. A major problem in the prevention of bacterial diseases is that dormant cells are not detectable by routine tests making them difficult to study. In this project, we will exploit new approaches to understand the mechanisms by which dormant bacterial Vibrio cells (our study organism) emerge as active disease-causing pathogens in the environment. The student will detect and quantify Vibrio species in their various functional states (active and dormant) from water, sediment, and shellfish sites from a coastal site in England and will require a background in microbiology. There will be opportunities to carry out field work and gain expertise in a broad range cutting-edge techniques including flow cytometry, cell sorting, and preparing samples for Mass spectrophotometry (LCMS) for studying chemical signals involved in bacterial dormancy.  This project will be flexible and will be developed together with Dr Sariqa Wagley depending on the strength and interests of the PhD student.  

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The closing date for applications is 2359 hours GMT Tuesday 9 January 2024.

CDTS303: Coastal Vigilance: Harnessing AI, Remote Sensing, and Citizen Science for Enhanced Observation and Monitoring of Land-to-Coastal Pollutant Transport

Lead Supervisor
Iestyn Woolway, University of Bangor

Additional Supervisors
Stefan Simis, Plymouth Marine Laboratory

Our world's coastal zones, brimming with vital ecosystems, face a pressing threat: the unrelenting transport of pollutants from land to sea. Dive into the cutting-edge realm of our PhD project, where we are at the forefront of change, marrying Artificial Intelligence (AI), remote sensing technologies, and the power of citizen science to revolutionise the way we observe and monitor the movement of pollutants. By harnessing these ground-breaking forces, we're on a mission to safeguard these delicate ecosystems, empower informed decision-making, and shape a more sustainable future.

Find out more and apply
 

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Scenario Doctoral Training Partnerships

Treatment of model bias in Marine Ecology forecasting

Lead supervisor:
Alison Fowler from Uni of Reading/NCEO

Co-supervisors

David Ford - Met Office
Dr Jozef Skakala - PML
Prof Amos Lawless, Dr A Fowler.

Location: This studentship is a joint project with the Met Office and Plymouth Marine Laboratory (PML). The student will have the opportunity to spend time working at the Met Office and PML over the lifetime of the project. The student will also have the opportunity to attend ECMWF training courses on data assimilation and advanced training courses at Reading organised by the Data Assimilation Research Centre and the National Centre for Earth Observation

The monitoring and forecasting of marine ecology are essential to understanding the current and future health of our coastal oceans. Long-range forecasts allow for the assessment of the impact of climate change on ocean biomass and pH trends, while shorter time-scale forecasts can help predict sudden dangerous events, such as harmful algal blooms, or oxygen depletion. The forecasts are generated using sophisticated numerical models of marine biogeochemistry. To enable the numerical models to stay in line with the true underlying system a technique known as data assimilation (DA) is used to systematically blend the model with observations made from a myriad of instruments. One rich source of observations comes from satellite-derived sea surface chlorophyll concentrations (see figure), a proxy of how much life there is in the ocean. DA algorithms are based on mathematical principles that make approximations about the uncertainty in both the model and observations. One of the most fundamental approximations is that both are unbiased estimates of the true underlying system (i.e. they neither systematically over- nor under-estimate). Unfortunately, this can be far from true for marine ecology models due to the complex biogeochemical processes they need to represent [Fowler et al 2022]. As such, model biases are a significant limitation of the skill of marine ecology forecasts. Different approaches to treating bias within DA exist, but in order to apply these techniques an estimate of the bias or its statistics is needed. This is particularly challenging given that the biases are likely to have high variability in space and time. In order to tackle this challenge of marine ecology forecasting, you will collaborate with marine ecology and data assimilation experts from the Plymouth Marine Laboratory, the Met Office, and the National Centre for Earth Observation. You will utilise the latest advancements in machine learning (ML) in order to predict the model bias. Even with the most sophisticated ML techniques, it is anticipated that there will still be limitations on how good the estimate of the model bias is due to the uncertainties and incomplete coverage of the available observations that can be used to train the ML algorithms. For example, the satellite-derived sea surface chlorophyll concentration data can itself have large biases and few observations are available of the sub-surface or of key variables other than chlorophyll. The second aim of this project is therefore to develop DA techniques that can address model biases while being resilient to the inherent limitations of the estimated model biases. Once developed, these techniques will be applied to a real-world marine ecology forecasting system.

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The closing date for applications is 2359 hours GMT Tuesday 9 January 2024.