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Seascape genomics of Antarctic deep-sea coral: Groundtruthing larval dispersal models with genetic connectivity data


Project Description

Scientific background
Detailed understanding of dispersal and genetic connectivity is critical in determining processes underpinning population persistence and productivity, speciation, appropriate scales for management, and the potential for recovery from detrimental impacts e.g. climate change and/or fishing.

Larval dispersal models (LDMs) integrate mathematical hydrodynamic models with species’ biological data to predict population connectivity. They are economical, in time and effort, compared to genetic connectivity research (no sampling/expensive laboratory analyses). For this reason, LDMs are increasingly used in marine environments to investigate connectivity (Ross et al., 2016; 2019), especially in areas challenging to sample, e.g. deep sea. However, very few LDMs are validated with genetic connectivity data. This project creates LDMs and then compares outputs with ground-truthed genomic connectivity data - a combined approach called “seascape genomics” (Selkoe et al., 2016). By using environmental data alongside genomic data, the drivers of connectivity across this rapidly-changing region are investigated. The study focuses on deep-sea octocorals from sub-Antarctic UK overseas territories – some are MPAs giving this project an applied output with great potential for management impacts.

Research methodology
Collate oceanographic and environmental datasets and investigate the utility of various oceanographic models, combined with Lagrangian particle simulators, to predict larval dispersal in deep-sea octocorals. Compare dispersal model outputs with known genomic connectivity between study sites. Research will be undertaken at UoE using a high performance computing server. On regular visits to Cefas, model utility will be assessed and outputs integrated into practical protection measures.

Training
Mathematical modelling – Oceanographic models and Lagrangian particle simulators.
Mapping/geographic data analyses skills using ArcGIS / QGIS / R.
Analysing genomic connectivity data – STACKS, BAYESCAN, STRUCTURE, and adegenet in R.
Communicating science to policy makers (minimum 3 months at Cefas).

Person specification
This PhD suits a quantitatively-minded candidate. Suitable degrees could cover topics such as genetics, mathematics, physics and/or biologists with an interest in modelling. Essential - some experience in statistical or mathematical modelling. Desirable - working knowledge and experience in R/ Matlab, an interest in deep-sea ecology.

How to apply
Please apply by sending a CV (including contact details of two academic referees) and a cover letter explaining your motivation and suitability for the PhD to Emma Revill . If you have any questions please feel free to contact any member of the supervisory team.



Funding Notes

This project has been shortlisted for funding by the ARIES NERC Doctoral Training Partnership, and will involve attendance at mandatory training events throughout the course of the PhD.

Shortlisted applicants will be interviewed on 18/19 February 2020.

Successful candidates who meet UKRI’s eligibility criteria will be awarded a NERC studentship - UK/EU nationals who have been resident in the UK for 3 years are eligible for a full award.

Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for additional 3-month stipend to take advanced-level courses in the subject area.

Further information, visit View Website

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