Dr David Barnes (British Antarctic Survey)
Professor Martin Attrill (School of Biological and Marine Sciences, University of Plymouth)
Professor Louise Allcock (National University of Ireland Galway)
The role of marine ecosystems in global carbon storage and burial is a topic of considerable interest given the urgent need to address the climate crisis. The ‘Blue Carbon’ concept considers all the biological carbon captured by marine living organisms, and represents over half of all biologically captured carbon. While much research has been focused on coastal angiosperm and algal dominated systems, the importance of animals in mediating biogeochemical processes and their effects on carbon storage and exchange have been noted. Recently the role of coastal marine animal forests as potential carbon sinks has been highlighted. The coastal environment represents only a fraction of the ocean. Most of the ocean (~90%) is considered deep sea. Within the deep-sea biome the presence of various animal forests is well documented. These animal forests tend to be dominated by long-lived black, bamboo, and gorgonian corals, or large structure forming sponges. The potential role of deep-sea animal forests in the carbon budget is largely unknown, but the vast size of the deep-sea ecosystem suggests that role may be significant.
This studentship will focus on providing the first estimates of the role of deep-sea animals in the carbon budget using a range of lab, deep-learning, and modelling techniques. The student will quantify the organic carbon content of selected deep-sea species, apply deep-learning techniques to deep-sea image and video analysis to generate species density datasets, model the density of species at the Atlantic basin scale, and map the spatial distribution of carbon sinks assessing MPA importance. Depending on their background the student may receive training in lab based skills, ecology and taxonomy, computer vision, machine learning, R and / or Python programming, habitat suitability modelling and ArcGIS. A degree in either an ecological field or highly numerate field e.g. mathematics is required.
We are looking for someone with a strong mathematical background and a demonstrable capacity to learn new skills and adapt their knowledge to new situations. Skills in use of statistical and / or computational models (for example one or more of the following – GLMS, GAMS, machine learning, convolutional neural networks) are essential.