The QMEE CDT Project proposal database

Welcome to the QMEE CDT Project proposal database. This is a live list of projects proposals put forward by PIs across the CDT partner institutions

PIs/Supervisors will continue to add projects to this list over the next few months, so do keep checking back! You can search the projects using the box below: simply enter some text and press Search to do a text search across all the database fields. If you want to search more finely, the search tool also allows you to search on particular details of the project descriptions: you will see these finer search options appear if you click on the search box.

Click on the view button next to a project to get the full proposal description. If you want to download project details, either for all projects, or for a subset you have searched for, then click on the 'Download details' button.

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Investigating marine harmful algal population dynamics through laboratory cultures and biogeochemical modelling
Marine harmful algae cause huge economic and ecological damage through the production of toxins and deoxygenation of marine waters, affecting the marine wildlife and commercial aquaculture. Current models for prediction of harmful algal population dynamics are not sufficient due to lack of species-level data and physiological responses to their environment, but bloom prediction is particularly important with climate change. Species-interaction models suggest that allelochemical or toxin producing algae are important for the diversity of the primary producers and grazers, and for regulating the dynamics of lower-trophic interaction [1-2]. But the representation of certain keystone harmful algae species, such as those that produce toxin or allelochemicals are not explicit within the advanced ocean biogeochemical models. So, it is essential that the dynamics of these species are included in the biogeochemical models, and the wider impact of toxin production is explored across marine ecosystems. This interdisciplinary project between the UoR, Cefas and the Marine Biological Association (MBA) aims to improve understanding of marine harmful algal dynamics by using historic and current plankton data, and employing laboratory culture, toxin and DNA analysis and statistical/mathematical modelling. Physiological studies of local harmful algae isolates will be used to better understand their preferred conditions and toxin status. These data can then be synthesised with models of harmful algae and larger scale biogeochemical models to provide invaluable information to marine managers, food safety and fisheries. The student will (1) perform controlled laboratory culture experiments with a list of key harmful algae species found in the UK waters, and determine their toxicity in different growth conditions, to provide a better understanding of physiological preferences of these algae (year 1); (2) measure harmful algae patterns at the species and population level in the English Channel ecosystem regions around the UK using a 60-year archive of plankton samples (year 1,2); (3) build dynamic interaction models among HAB-producing and non-producing phytoplankton species and calibrate the models based on data from laboratory experiments (year 2,3); (4) advance an ocean biogeochemical model by incorporating the calibrated species-interaction model components (year 3). The student will work at the interface between lab experiments/molecular analysis at Cefas and the MBA, and computer-based modelling at the UoR. The student will benefit from a fully equipped molecular lab and a culture collection of phytoplankton maintained at thermostatically controlled incubators, and genetic assays at the MBA with Stern [3], an advanced biotoxin analysis lab and research at Cefas with Turner [4], and the ongoing biogeochemical modelling studies at UoR with Roy [1,2,5]. Some experience in related field either experimental or modelling, and numerical analysis/coding (e.g. FORTRAN/MATLAB) would be useful. But in-house training on numerical ecology and experiments will be available to a motivated candidate. References: (1) Roy (2015) In: Biodiversity in Ecosystems - Linking Structure and Function. INTECH, 17-28. (2) Felpeto et al. (2018) Oikos 127(1) 85-98. (3) Stern et al. (2018) Journal of Plankton Research 40 (5), 519-536. (4) Turner et al. (2019) Frontiers in Physiology 10 (373), 1-11 (5) Anugerahanti et al. (2018) Biogeosciences 15, 6685-6711.
Shovonlal Roy
Andrew Turner
Rowena Stern; Marine Biological Association;
Development of mathematical theory, Computing, Quantitative data analysis, Ecological observations / data collection
Shovonlal Roy
The student will learn mathematical modelling and software packages for simulation and visualization of biogeochemical models; will benefit from UoR’s Researcher Development Programme; and will learn coding in python, FORTRAN and MATLAB. These will be relevant to NERC’s most-wanted cross-disciplinary skills: modelling; utilization of complex environmental data; quantitative science.
The project will develop innovative interaction models based on laboratory experiments to improve the existing ocean biogeochemical models. The innovation will also come from new data and insight on how harmful algae function in controlled environment and how it impacts larger marine ecosystems.
The project will test the fundamental theories resource competition, species coexistence and biodiversity, in relation to ocean primary producers and grazers, and will improve the existing ocean biogeochemical models to better predict the species diversity at the microscopic level.
This project will contribute to understanding of the ecology of certain types of harmful bloom-forming algae, and their consequences on marine ecosystems. The outcome will be directly relevant for coastal management, water clarity and management of marine economic resource e.g. fish, and food safety.
This project will provide new information on the formation of harmful algal bloom and their wider consequences on marine ecosystems, which will be directly useful for coastal management of harmful algae. The results will also impact the studies and management of marine economic resources such as fish, and help predicting coastal water clarity, and food safety.
The project is multidisciplinary, it involves laboratory experiments, ecological modelling, statistical methods, and biological oceanography. The student will benefit from the multi-disciplinary supervisory team working on ecological modelling and data analysis (S. Roy), molecular genetics (R. Stern) and marine biotoxin (A. Turner).
Marine environments, Environment and health, Environmental microbiology
Training will be given on building mathematical models, parameter estimation, model simulation, as well as laboratory culture of algae, water chemistry, analysis of bio-toxin, DNA analysis.
MBA, Cefas and UoR
2019-05-31 13:41:59