Abyssal megafaunal resilience to deep-sea mining

Dr Daniel Jones, Dr Erik Simon Lledo, Dr Jon Copley
Rationale: 

Many deep sea habitats have characteristics that suggest they may be particularly susceptible to disturbance and may recover slowly, such as relative stability, low temperatures, food supply and biological rates. Yet, some of these habitats are about to be subjected to unprecedented levels of direct anthropogenic disturbance from deep-sea mining, with unknown consequences for their ecosystems. There is an increased need to understand how these deep-sea ecosystems respond to and recover from broad-scale disturbance. This project will specifically evaluate ecosystem resistance and resilience to disturbance from seabed mining in the Clarion-Clipperton Zone (CCZ) of the Pacific. It will focus on the seafloor megafauna, an important group in deep-water systems and a group that will be an important component of baseline assessment and monitoring. 

Methodology: 

The project will build on a broad base of existing baseline data for the CCZ through an extensive field campaign in the tropical Pacific. The studentship will form part of a large NERC project: Seabed Mining And Resilience To EXperimental impact (SMARTEX) with a wide range of scientists from 9 UK institutions.

The student will join a large shipboard field campaign to evaluate the abyssal seafloor using a suite of marine robotic systems. The project will use two unique opportunities for field experimental assessment of mining impacts: firstly from a four-decade old mining test carried out in the region, data for which have only just been re-discovered, and secondly from a new mining test scheduled for the eastern CCZ.

The project will focus on broad-scale patterns in benthic megafauna, evaluated using remotely operated vehicle and autonomous underwater vehicle photography. The student will quantify patterns in megafaunal assemblages across a disturbance gradient created by mining activities of several ages. Assessing real mining disturbances will significantly advance the limited existing knowledge, which has primarily focused on evaluating different disturbance types in different areas.

There will be opportunities for wide scientific collaboration and direct interaction with deep-sea mining companies, government and regulatory processes.  

Location: 
NOC Southampton
Training: 

The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and hosted at the National Oceanography Centre, Southampton.

The student will receive training in seagoing sample collection from a research vessel. They will also receive training in the analysis of photographic and sample data and subsequent interpretation. This will include processing and identification of megafaunal data from both images and specimens, data processing and analysis using a range of appropriate statistical techniques. The student will also receive training in using the R programming language for statistical analysis and data processing and ArcGIS software. There will be opportunities to present scientific findings at a range of relevant scientific and policy forums. 

Eligibility & Funding Details: 

Please check https://inspire-dtp.ac.uk/how-apply for details.  

 

Background Reading: 

Jones, D.O.B., Amon, D.J., Chapman, A.S.A., 2018. Mining Deep-Ocean Mineral Deposits: What are the Ecological Risks? Elements 14 (5), 325–330.

Simon-Lledó, E., Pomee, C., Ahokava, A., Drazen, J.C., Leitner, A.B., Flynn, A., Parianos, J., Jones, D.O.B., 2020. Multi-scale variations in invertebrate and fish megafauna in the mid-eastern Clarion Clipperton Zone. Progress in Oceanography 187, 102405.

Simon-Lledó, E., Bett, B.J., Huvenne, V.A.I., Köser, K., Schoening, T., Greinert, J., Jones, D.O.B., 2019. Biological effects 26 years after simulated deep-sea mining. Scientific Reports 9 (1), 8040.

 

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