Dr Michelle Taylor, School of Life Sciences, University of Essex
Dr Oliver Hogg, Centre for Environment Fisheries and Aquaculture Science (Cefas)
Dr Rebecca Ross, Institute of Marine Research, Norway
CASE studentship to investigate connectivity of Southern Ocean deep-sea coral; a rare opportunity to verify larval dispersal models with genomic data.
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.
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.
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).
This PhD suits a quantitatively-minded candidate with some experience in statistical or mathematical modelling. Suitable degrees could cover topics such as genetics, MTH, physics and/or biologists with an interest in modelling. Essential – working knowledge and experience in R/ Matlab, Desirable – an interest in deep-sea ecology.