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Combining Evidence for Marine Monitoring (KULINSKAYAUCMP19ARIES) [CASE project with Cefas]


Project Description

This is a CASE project with Cefas.

Scientific background
Cefas and the wider monitoring community is increasingly looking to fulfil monitoring objectives using a combination of data sources. For example: Cefas has a unique dataset of 130 years of sea temperature data, containing over 10 million records from 17 data systems; data collected for seafloor litter monitoring is performed with a variety of different fishing gears; eutrophication monitoring is collated from devices installed on board vessels, deployed on automated systems or estimated from satellite imagery.

Research methodology
This project will use publicly available datasets. The available data are described in: [1] (eutrophication), [2] (litter) and [3] (temperature).
The objective of this project is to combine data from different sources in a way that results in statistically unbiased estimates that can be used to assess spatial and temporal trends [4].
Two-stage analytic methods will be developed: firstly, a common dataset-specific model is developed and secondly, meta-analytic methods are used to pool the model parameters across datasets [5].
For the sea temperatures, the aim is to evaluate changes over time but making sure that these changes are real and not functions of the assessment methods used. With eutrophication, the question is whether and how to combine results measured using different methods. With litter, the main challenge is to create spatially and temporally coherent estimates of sea floor litter (principally plastic) levels without confounding by the different trawl methods used to collect the data.
The research will start with sea temperatures and then move to other areas if needed.

The student will be based at the UEA School of Computing Sciences, where there is a considerable expertise in Big Data analysis. They will also spend a significant amount of time at Cefas in Lowestoft, where the datasets are curated and where their co-supervisors are based.

Training
The student will receive advanced training in statistics, Big Data and environmental sciences. They would also receive help and guidance on the environmental and ecological aspects of the work from scientists at Cefas.

Person Specification
Applicants should have a minimum 2:1 Bachelor degree in statistics, mathematics or another numerical discipline, and strong computing skills.

Start Date: October 2019
Mode of Study: Full-time or Part-time
Studentship length: 3.5 years
Minimum entry requirement: UK 2:1


Funding Notes

This project has been shortlisted for funding by the ARIES NERC Doctoral Training Partnership. Undertaking a PhD with ARIES will involve attendance at training events.
ARIES is committed to equality & diversity, and inclusion of students of any and all backgrounds.
Applicants from quantitative disciplines with limited environmental science experience may be considered for an additional 3-month stipend to take appropriate advanced-level courses. Usually only UK and EU nationals who have been resident in the UK for 3 years are eligible for a stipend. Shortlisted applicants will be interviewed on 26th/27th February 2019.

Further information: View Website or contact us:


References

1. Heffernan J, Barry J, Devlin M and Fryer R (2010). A simulation tool for designing nutrient monitoring programmes for eutrophication experiments. Environmetrics, 21, 3-20.
2. T. Maes, J. Barry, H.A. Leslie, A.D. Vethaak, M. Nicolaus, R. Law, B. Lyons and J. Thain (2018). twenty-five years of seafloor litter monitoring in coastal seas of North West Europe (1992-2017). Science of the Total Environment, 630, 790-798, https://doi.org/10.1016/j.scitotenv.2018.02.245.
3. Morris, D. J., Pinnegar, J. K., Maxwell, D. L., Dye, S. R., Fernand, L. J., Flatman, S., Williams, O. J., and Rogers, S. I.: Over 10 million seawater temperature records for the United Kingdom Continental Shelf between 1880 and 2014 from 17 Cefas (United Kingdom government) marine data systems, Earth Syst. Sci. Data, 10, 27-51, https://doi.org/10.5194/essd-10-27-2018, 2018.
4. Dogo, S. H., Clark, A. & Kulinskaya, E. (2017). Sequential change detection and monitoring of temporal trends in random-effects meta-analysis. Research Synthesis Methods, 8, 220-235.
5. Gasparrini A, Armstrong B, Kenward MG. 2012a. Multivariate meta-analysis for non-linear and other multi-parameter associations. Statistics in Medicine, 31:3821-3839.


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