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Assessing benthic recovery following cessation of salmon farming using eDNA metabarcoding (ABReDNA)

Scottish Association for Marine Science

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Dr T A Wilding , Prof T Telfer No more applications being accepted Funded PhD Project (Students Worldwide)

About the Project

Salmon farming is the biggest UK food export sector with ambitions to double output by 2030. To increase production, the salmon farming sector is likely to move further offshore, and close existing near-shore sites. During salmon production, waste material from the salmon changes the sediment under the cages, the extent of which is monitored (as part of compliance assessments) by assessing faunal distributions.
At SAMS and the Rivers and Lochs Institute (RLI) we, together with our international partners, are developing metabarcoding-based alternatives to macrofaunal-based compliance monitoring which are equally applicable to assessing benthic recovery.
For this PhD we have partnered with SEPA and MOWI to gain access to samples being taken around a farm-site where operations have ceased. This is a fantastic, and unique, opportunity to track benthic recovery, using eDNA-based methods, and compare this to traditional methods. This innovative research is of considerable academic interest and has great publication potential.
In this PhD you will develop skills and expertise in the following:
1. Boat-based sampling (you will have the opportunity to join MOWI in their sampling at their Loch Ewe site).
2. Experimental design to optimise the sampling programme, optimisation in relation to available resources, consideration of true and technical /PCR replication, statistical methods for variance partitioning.
3. Laboratory-based DNA extraction, marker/primer selection, PCR-amplification, tagging/multiplexing (collectively DNA library preparation)
4. Laboratory-based sequencing, using RLI-UHI and SEPA’s Illumina MiSeq™ facilities
5. Application of bioinformatic algorithms to extract information from the sequence-data, including the annotation by comparison to sequence databases
6. Application of statistical modelling (both univariate and multivariate analysis) to quantify, display and communicate the data.
You will be based at SAMS, Oban but spend extensive periods at the UHI-RLI (Inverness, under the supervision of Barbara Morrisey, your third PhD supervisor), SEPA’s new sequencing facility (Angus Smith Building, Holytown) with visits to the University of Stirling. SAMS and the RLI host 70 marine-focussed PhD students and offers a thriving, vibrant, supportive environment. As part of this PhD you will be enrolled on a post-graduate certificate in Research Professional Development giving you the opportunity to develop communication, team-working and management skills. Training in coding/programming, data handling/wrangling and statistical modelling will be provided, as necessary.
You will build on existing national and international collaborations, working with experts in DNA preparation and sequencing, bioinformatics and statistics. There will be numerous opportunities to present your work at national and international workshops and conferences and you will benefit from both a national and international context to your PhD.
With the contacts made through the PhD partnership (SAIC, SEPA, MOWI) and the enviable skill set you will develop you will, once graduated, have tremendous employment potential across a wide range of environments (not only marine) and sectors (aquaculture, regulatory and academic).
Background reading
SEPA (2019)
Forster, D., et al. (2019). "Improving eDNA-based protist diversity assessments using networks of amplicon sequence variants." Environ Microbiol
Frühe, L., et al. (2020). "Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes." Molecular Ecology doi: 10.1111/mec.15434
Lejzerowicz, F., et al. (2015). "High-throughput sequencing and morphology perform equally well for benthic monitoring of marine ecosystems." Sci Rep 5: 13932.
Pawlowski, J., et al. (2014). "Environmental monitoring through protist next-generation sequencing metabarcoding: assessing the impact of fish farming on benthic foraminifera communities." Molecular Ecology Resources 14(6): 1129 - 1140.

Funding Notes
Funded by NERC Studentships awarded to the SUPER Doctoral Training Partnership. The SUPER DTP partner Universities are St Andrews University, Aberdeen University, Edinburgh Napier University, Heriot-Watt University, the University of the Highlands and Islands, Stirling University, University of Strathclyde and the University of the West of Scotland. Underpinning these research partners, providing additional training and projects are Marine Scotland, Scottish Natural Heritage, and the James Hutton Institute, among a total of 40 stakeholder organisations including industry and government agencies and international collaborators.

The start date of this project is: 27 September 2021

The 3½ year studentships cover:
• Tuition fees each year (UK fee rate only* - for 2020/21 this amounts to £4,407 for full-time study
• A maintenance grant each of around £15,000 per annum (for full-time study)
• Funding for research training
• Part-time study is an option, with a minimum of 50% of full-time effort being required.

*please note: for International students, there may be funding available to cover the full international tuition fee and this will be discussed at interview. If funds are not available, the candidate will be required to cover the difference in fees – for 2020/21 this difference amounts to £10,932. Annual tuition fees are subject to revision and typically increase by between 1.5-3% per annum.

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, or
• Other qualifications or experience that affords sufficient evidence of an applicant’s ability to work at the academic level associated with doctoral study.

• This PhD will require a high degree of coding/programming proficiency mainly in the R language. You will also be required to programme in BASH. Candidates should also have a good understanding of metabarcoding concepts and be prepared to undertake limited periods of field-sampling in Loch Ewe, Scotland.

Enquiries (Name, Phone number, Email address)

Project specific enquiries: [Email Address Removed]

General enquiries: Graduate School Office [Email Address Removed]

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