Acronym ECOKNOWS
Category
Fisheries
Title Effective use of ecosystem and biological knowledge in fisheries Programme FP7
Instrument (FP6)
Contact Type (FP7)
Strand (Interreg)
FP7 - Small or Medium-Scale Focused Research Project
Theme (FP7)
Activity Area (FP6)
Regional Area (Interreg)
Action (COST)
KBBE – Food, Agriculture and Fisheries, and Biotechnology
Specific Programme (FP7)
Cooperation
Funding source European Coordinator Sakari Kuikka Coordinator email sakari.kuikka@helsinki.fi
Coordinator institution
UH - University of Helsinki (Finland)
Institutions involved
AUTh - Aristotle University of Thessaloniki (Greece) ,
FGFRI - Finnish Game and Fisheries Research Institute (Finland) ,
FIN - FishBase Information and Research Group, Inc (Philippines) ,
DFO - Fisheries and Oceans Canada (Canada) ,
INRA - French National Institute for Agricultural Research (France) ,
ICL - Imperial College of Science, Technology and Medicine (United Kingdom) ,
NA - Institute for Life, Food and HorticUltUral Sciences and Landscaping (France) ,
ICES - International Council for the Exploration of the Sea (Denmark) ,
MI - Marine Institute (Ireland) ,
IEO - Spanish Institute of Oceanography (Spain) ,
CSIC - Spanish National Research Council (Spain) ,
SBF - Swedish Board of Fisheries (Sweden) ,
Start year 2010 End year 2014 Funding (€) € 3,793,491 Website https://cordis.europa.eu/project/id/244706 Summary The general aim of the ECOKNOWS project is to improve the use of biological knowledge in fisheries science and management. The lack of appropriate calculus methods and fear of statistical over-parameterization has limited biological reality in fisheries models. This reduces biological credibility perceived by many stakeholders. We solve this technical estimation problem by using up-to date methodology supporting more effective use of data. The models suggested will include important knowledge about biological processes and the applied statistical inference methods allow us to integrate and update this knowledge in stock assessment. We will use the basic biological data (such as growth, maturity, fecundity, maximum age and recruitment data sets) to estimate general probabilistic dependencies in fish stock assessments. In particular, we will seek to improve the use of large existing biological and environmental databases, published papers and survey data sets provided by EU data collection regulations and stored by ICES and EU member countries. Bayesian inference will form the methodological backbone of the project and will enable realistic estimations of uncertainty. We develop a computational learning approach that builds on the extensive information present in FishBase (www.fishbase.org). The developed methodology will be of fundamental importance, especially for the implementation of the Ecosystem Approach to Fisheries Management. It has been a difficult challenge even for target species with long data series, and now the same challenge is given for new and poorly studied species. We will improve ways to find generic and understandable biological reference points, such as the required number of spawning times per fish, which also supports the management needs in the developing countries. ECOKNOWS applies decision analysis and bio-economic methods to evaluate the validity and utility of improved information, helping to plan efficient EU data collection.
Keywords
Fish biology;
Stock assessment;
Marine Region
76
Not associated to marine areas
0
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