Anthropogenic pressure on deep sea habitats is growing, through fisheries, oil and gas exploration and climate change, including deep ocean temperature rising and ocean acidification. This increased pressure, coupled with the slow turn-over rates and limited resilience of deep-sea fauna have led to the global recognition of the urgent need to develop and implement effective deep-sea environmental management strategies. Recently a new industry, deep-sea mining, has also emerged and is set to become increasingly important over the next century. The deep-sea harbours rich deposits of precious metals and rare earth elements that are used in modern technologies. Polymetallic nodules, polymetallic sulphides and ferromanganese crusts are the key deposits of interest, and the International Seabed Authority, who licence and regulate mining activities in areas beyond national jurisdiction, have to date have entered into 15-year contracts for exploration of these resources with 21 contractors. 13 of these contracts are for exploration for polymetallic nodules in the Clarion-Clipperton Fracture Zone (CCFZ) in the Pacific Ocean. The areas involved are vast, and data poor, hampering the development of effective environmental management and conservation strategies.

Predictive habitat mapping and species distribution modelling are widely used tools in conservation ecology and environmental management. Examples include but are not limited to: identification of priority areas for conservation (Rodriguez-Soto et al., 2013), inferring potential climate change-induced range shifts in species distributions (Cheung et al., 2009; Bond et al., 2011; Hare et al. 2012), evaluating management scenarios and economic valuation (Bergström et al., 2013; Lindegarth et al., 2014) and predicting the spread of alien invasive species (Ficetola et al., 2007). The application of these methods to the deep-sea environment is relatively new (e.g., Vierod et al. 2014 and references therein) but has been achieved at a variety of scales and mapping both broad scale physical habitat types (Howell, 2010; Harris and Whiteway 2009), and fine scale species and assemblages (Howell et al. 2011; Rengstorf et al. 2013; Ross & Howell 2013) to inform spatial management decisions. It is a promising tool in deep-sea marine environmental management potentially reducing the cost of comprehensive field surveys by allowing targeting of important areas, and filling data gaps for large areas of un-sampled seabed (Elith & Leathwick 2009; Dambach & Rodder 2011; Robinson et al. 2011). However, there remain some key questions around the application of predictive models to different settings, the validity of the predictions, and their accuracy and performance at different spatial scales that require addressing before these methods can be used in an informed manor for deep-sea environmental management.

Similarly populations connectivity modelling is also a promising tool in marine environmental management. Population connectivity refers to the exchange of individuals among populations: it affects gene flow, regulates population size and function, and mitigates recovery from natural or anthropogenic disturbances. Many populations in the deep sea are spatially fragmented, and will become more so with increasing resource exploitation. Understanding population connectivity is therefore critical for effective spatial management. Coupled biophysical models, incorporating ocean circulation and biological traits, such as planktonic larval duration (PLD), have been used to estimate population connectivity and generate spatial management plans in coastal and shallow waters. However, this technique has also only recently been applied in the deep-sea with little understanding of its reliability in reflecting population relatedness.

We propose a research programme to investigate the potential for predictive modelling approaches to inform deep-sea spatial management. Using deep-sea mining and the CCFZ as a case study the PhD will address the following questions:

1. How well do broad scale physical habitat maps reflect variation in the biological communities?

2. To what degree of reliability can we predict the distribution of faunal assemblages in the CCZ at regional and site specific spatial scales?

3. How well do coupled biophysical models reflect patterns of population connectivity in the CCZ as measured by molecular studies?

The supervisory team is composed of members from three institutions including Plymouth University, UK, the Natural History Museum, UK, and the University of Hawaii, USA. Each institution brings different expertise to the team. Dr Kerry Howell (Plymouth) is the Director of Studies and has expertise in deep-sea ecology, distribution modelling, habitat mapping and marine conservation. Professor Martin Attrill (Plymouth) is a co-supervisor and has expertise in seabed surveying and analysing marine benthic biodiversity patterns in time and space. Professor Craig Smith (Hawaii) is a co-supervisor and has expertise in ecology and biodiversity of marine sediments. He also lead the Kaplan project 2002-2007, the first and most successful attempt to analyse species composition and rates of gene-flow of abyssal organisms across the abyssal plains of the CCZ. Dr Adrian Glover (NHM) is a co-supervisor and has expertise in macrofaunal biodiversity and species ranges over large scales in the CCZ. The team will support the student in developing the necessary skills to undertake a significant body of research. The student would be based at Plymouth University, UK, but will be expected to spend six to nine months in Hawaii working alongside members of the Abysslines project and applying their data to model validation. The student will be expected to participate in at least one research cruise to the CCZ.

The student would be highly numerate and require a degree in Marine Biology, Oceanography or related subject at 2:1 level or above. A masters or equivalent work experience in a relevant field would be desirable. Experience of undertaking benthic surveys, use of GIS, R, statistical modelling, Lagrangian particle dispersal modelling, and / or skills in species identification are also desirable; English (spoken and written) to a level of at least IELTS 6.5 (with a minimum 5.5 in each category, reading, writing, listening and speaking).

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