Spatial management decisions of marine areas require highly-resolved information about human activities and their impacts on the marine environment. Equally important, is the need for integrated, ecosystem-level analysis of these data that can be understood and used by managers and policy-makers. Empirical data of this type are not always available at the ecosystem scale, depending on vast amounts of funding, and cross-border coordination of monitoring programmes. Alternatively, macro-scale models implemented in VECTORS have the potential to assist this task, providing a holistic view of ecosystems against which the impacts of conservation, management and global scenarios can be assessed. While this information exists, the challenge lies in converting complex, detailed ecosystem-level research into simple products that can be communicated to management, addressing questions of practical use: where is change happening and how big is it? Such integrated data products are high in demand and short in supply. To meet this need, a novel approach to meta-analysis was developed in WP4.2, bringing together a diverse range of modelling projections of macro-scale dynamics of the North Sea for the next 50 years, associated with climate change and coastal nutrient loading.
This approach has now been proposed a a potentially relevant method for marine spatial planning in the German EEZ.
General trends of ecosystem-level change were identified and interpreted in the context of projected marine spatial planning (MSP) actions for the region. Ares projected to be most vulnerable to unmanageable pressures such as climate change are being overlooked by MSP. This finding has important implications for management of the North Sea ecosystem and the sustainable use of its resources. This novel way of integrating information makes complex ecosystem modelling projections easily available to inform marine ecosystem-based management.
The findings of this analysis can be found in D4.2.1. The meta-analysis of modelled fish species distributions (54 model outputs) indicated that change will occur predominantly via relocation of biomass towards the NE of the North Sea, possibly because species will track areas where warming will be less pronounced within the next decades. Alternatively, the meta-analysis of lower trophic level model projections (11 model outputs) suggested that greater and negative change will occur in southern, coastal areas associated with increased nutrient loads, and in the NW of the North Sea associated with changes in the Atlantic front. When the two data sets were combined, the most significant, negative, ecosystem-level changes were calculated for the NW of the North Sea.These negative changes within the next decades suggest some degree of connectivity between lower and higher trophic levels, at the ecosystem scale in this area, associated possibly also with change in the productivity of lower trophic levels in the Atlantic front. This is in line with recent empirical findings for the area, suggesting that long-term changes in the dynamics of this water mass in the near future will lead to negative, ecosystem-level changes to the North Sea.
With regard to marine spatial planning, a mismatch between the distribution of projected conservation zones in the North Sea (see D4.2.1 for details) and hotspots of change in the North Sea identified in this study was observed . Overall, the proportion of these hotspots of change that is aimed for protection varied between 0 and 33% and in half of those cases, between 15 and 31% of the area was zoned for windfarm development. Some of these areas corresponded to the most severe hotspots of change identified here with regard to magnitude of change in this analysis. The patterns of change identified in this study suggest the need to consider long-term ecosystem dynamics (particularly as part of Environmental Impact Assessments) when decisions with long-term implementations such as (such as the implementation of wind farm developments) are made.
Overall, the majority of hotspots of change identified did not correspond to areas currently protected or under consideration for future protection.The fact that, in many cases, hotspots of change were both areas projected for protection and windfarm development is a consequence of the scale at which the datasets were aggregated. This aggregation was however necessary to integrate the very diverse sources of information considered in these analyses. The potential conflict areas identified here can serve as indicators of areas where more finely resolved analysis should be conducted to more clearly identify specific challenges to effective planning.