Network of scientists
EUR-OCEANS (European Network of Excellence for Ocean Ecosystems Analysis) was a network of excellence co-funded by the Sixth Framework Programme for Research and Technological Development of the European Communities (FP6). The specificity of the Networks of Excellence is that they are not simply an additional research programme. Their purpose is to structure/federate European research in an area of excellence.
EUR-OCEANS started in January 2005, running for 4 years until December 2008. It gathered more than 500 researchers from 60 research institutes and universities from 25 countries. The goal of this programme ended in 2008, was to bring together scientists studying the impact of climate change and human activities on marine ecosystems.
EUR-OCEANS focused on seven systems
- Arctic Seas
- Baltic Sea
- Mediterranean Sea
- North Atlantic
- North Atlantic shelves
- Southern Ocean
- Coastal upwelling areas
- To understand as well as possible, the effects of climate changes and anthropogenic forcing on the marine environment, fish and other living resources.
- To predict the impacts of those pressures on marine pelagic (i.e. offshore) ecosystems in the near future (next 50 to 100 years).
The latter is a key scientific, economic and social problem. Indeed, 60% of the World's ecosystems are degraded, especially marine ecosystems, and these are crucial for climate regulation and as food resources. Changes during the coming decades are likely to be very problematic.
The approach will be through modelling techniques. Models are the only way to make predictions by changing input variables. For example, what would happen if the concentration of CO2 in the atmosphere was multiplied by 2? By 4?
EUR-OCEANS simultaneously use global and regional approaches. The first helps to create models that simplify the complexity of the natural environment to thus allow the simulation of past and present conditions, and the prediction of changes on a large scale. The second, focused on economically or ecologically sensitive environments, allows us to understand and predict changes on smaller scales.