Larval dispersal modelling as a tool to support sustainable management of the deep sea


Demands on the resources of the deep sea have increased in recent years with both fishing and oil and gas activities pushing deeper, and the development of new industries like deep-sea mining. National and international policy commitments associated with these industries require the application of spatial management measures including the establishment of networks of Marine Protected Areas (MPAs) to help manage the activities, and protect biodiversity. Marine environmental managers are currently faced with the task of developing MPA networks that are ‘ecologically coherent’, one facet of which is population connectivity. The research base available to them to draw upon is, however, extremely sparse. 
Connectivity is best assessed using molecular tools (e.g. population genetics), however, this requires sufficient physical samples to enable DNA analysis of relatedness of individuals, and collection of specimens is expensive and time consuming. A cheaper and faster alternative is to model dispersal of propagules using Lagrangian particle tracking, driven by hydrographic models. This method enables rapid testing of hypotheses and different potential scenarios. However, it is unclear to what extent such models reflect realised population connectivity in the deep sea, and how such methods might be incorporated into spatial management and planning. 


The overarching aim is to provide the evidence base, in the form of peer reviewed papers and best practice guidance, to enable managers to operationalise consideration of ‘ecological coherence’ in area-based management of marine ecosystems. 


H1: Regional hydrodynamic model predictions reflect connectivity determined using empirical genetic data and to some extent, faunal similarity of sites at regional scales derived from community level data (data chapters 1 and 2).
H2: Existing example regional MPA networks are connected and may support potential recovery of regional marine biodiversity through larval supply (data chapters 3 and 4).

Brief methodology

The student will build on research conducted under a previous NERC project, DeepLinks, and an existing NERC-funded project, SMarTEx. DeepLinks focused on the UK deep-sea MPA network, while SMarTEx focuses on the MPA network established by the International Seabed Authority in the Clarion-Clipperton Fracture Zone in the central Pacific, as mitigation for the onset of deep-sea mining activities. 
Both studies used or will use Lagrangian particle tracking (LPT), driven by hydrographic models, to investigate connectivity of deep-sea MPA networks. In addition, both projects investigated genetic connectivity of particular taxa, and the similarity of benthic communities between MPAs (and for SMarTEx, licenced areas). In this study, the student will use these existing datasets to undertake a combined analysis of inter-site connectivity and relatedness for each MPA network as separate case study areas, pulling together all three data streams (genetics, LPT, and community data) to address H1. 
To address H2, the student will again focus on two case study areas. For the CCZ and working with the SMarTEx project, they will model larval dispersal from the MPA network to UK (and others) mining licenced areas of seabed to quantify the potential role of the MPA network in site recovery through provision of larvae. Modelling will also be conducted in reverse to look at origins of larval supply to licenced areas and thus possible sites for new MPAs that may help promote site recovery. A second case study area will focus on the UK’s MPA network in the South Atlantic (UK Overseas Territories). The student will model larval dispersal from the large MPAs of Ascension, St Helena and Tristan da Cunha to understand the potential role of these MPAs in supporting regional biodiversity maintenance.