There is a global issue of balancing resource exploitation with environmental protection. In particular, the marine environment is subject to many anthropogenic pressures which are most severe in coastal zones. Authorities managing these pressures have limited time and resources, so it is essential that they have access to data and modelling tools which help them prioritise their efforts.
This study presents a spatial modelling approach which draws together a range of key criteria into a single framework to identify marine areas which should be prioritised for management and monitoring. The study area, Sussex coastal waters (southern UK), was assessed through quantification and modelling of relative environmental score and fishing pressure score. Environmental score was assessed by combining ecosystem services provision, habitat diversity and sensitivity, based on seabed habitat data. Fishing pressure was assessed by combining fishing benefits, impacts and effort for specific local fisheries. The marine priority assessment was compared to the location of Marine Protected Areas to understand the relationship with existing management measures.
High and very high priority classes covered just 5% of the study area, with the highest priority area between Selsey and Bognor Regis. These habitats were ones found to have high environmental score (rocky reefs and seaweed-dominated sediment) concurrent with high fishing pressure. This modelling approach suggests that these areas should be the focus of further research, monitoring and potentially management measures. There was no significant difference between the priority score inside the MPAs and those outside, however, the environmental score was significantly higher inside MPAs. These findings suggest current MPAs are protecting valuable and/or sensitive habitats and management within these sites may have resulted in less fishing pressure.
Each multi-criteria element of the study individually advances our understanding of the value of this marine environment and the importance of fisheries in Sussex coastal waters. Together, the multi-criteria approach strengthens the knowledge of processes and interactions, building a robust evidence base for management decision making. A framework has been developed which, with the use of different or additional datasets, could be applied to many scenarios supporting environmental managers worldwide.