Analysis and Forecast in Marine Systems

In November 2013, the European Parliament and the European Council adopted the 7th EU Environment Action Program (7th EAP) to 2020, ‘Living well, within the limits of our planet’. This program is intended to help guide EU action on environment and climate change up to and beyond 2020. Several priority objectives refer to climate change adaptation. Several initiatives work on the provision of authoritative information about the past, present and future climate in Europe and the rest of the World (e.g. Copernicus Climate Change Service (C3S)). The BigDataOcean project for example strives to capitalise on modern technological innovations, utilising them to revolutionise the way maritime-related industries work. These innovations will enable the creation of an entirely new value-chain, which will lead to great economic, societal, and environmental impact.

The IPCC reports highlight the importance of analysis of current data sources and using this knowledge to forecast future climate, with a specific section on the assessment of the oceans.
The European Union (EU) is the world’s largest maritime territory and marine resources make a significant contribution to each Member State’s economic prosperity and social well-being. The European marine environment must therefore be protected to ensure that it is healthy, productive and safeguarded for the use of future generations. Many of the threats to Europe’s marine resources require cooperation and collective action to be tackled effectively. It is within this context that the Integrated European Maritime Policy, which aims to provide a coherent framework for joined up governance of the marine environment, has been developed.

Quantitative Marine Ecology

Measuring, analysing and modelling marine ecosystems

Marine Ecosystems Functioning and Changes: from fundamentals to experiments and monitoring

Students will gain theoretical and practical knowledge of the quantitative tools necessary for describing, analysing, and forecasting marine populations, communities and ecosystems.

Learning Outcomes:

The graduated student understands basic patterns in spatial or temporal distribution of organisms in their habitats.
The graduate student understands biophysical interactions constraining the various organism’s community structures at different scales.
The graduate student understands the theoretical foundation of advanced methods for the study of community change over space and time using large data sets and to identify key players and key relationships. They will be able to carry out these analyses using state of the art methods and tools.
The graduate student is able to use models in the context of ecosystem studies and evaluation and resources management. They will know the diversity of models and be able to make appropriate choices; understanding the basic mathematics behind modelling; identify challenges from a methodological point of view; design simple models; coding models and exploit numerical simulations.