The available database comprises research projects in Fisheries, Aquaculture, Seafood Processing and Marine Biotechnology active in the time period 2003-2022.
BlueBio is an ERA-NET COFUND created to directly identify new and improve existing ways of bringing bio-based products and services to the market and find new ways of creating value from in the blue bioeconomy.

More information on the BlueBio project and participating funding organizations is available on the BlueBio website: www.bluebioeconomy.eu

Last Update: 2024/06/19

CAFE
Fisheries
Capacity, F and Effort
FP6
 FP6 - Specific Targeted Research Project
Research for Policy Support
European
Anna Korre
a.korre@imperial.ac.uk
ICL - Imperial College of Science, Technology and Medicine (United Kingdom)
ARMINES - Association for the research and development of industrial methods and processes (France),  NA - Azti (Spain),  CEFAS - Centre for Environment, Fisheries & Aquaculture Science (United Kingdom),  FRS - Fisheries Research Services (United Kingdom),  IRD - French National Research Institute for Sustainable Development (France),  IFREMER - French Research Institute for Exploitation of the Sea (France),  SNF - Institute for Research in Economics and Business Administration (Norway),  JRC - Joint Research Centre (Belgium),  NA - National Research Council - Institute of Marine Engineering (Italy),  IEO - Spanish Institute of Oceanography (Spain),  DTU - Technical University of Denmark (Denmark),  KU - University of Copenhagen (Denmark),  NA - University of Portsmouth (United Kingdom),  WUR-IMARES - Wageningen University and Research; Institute for Marine Resources and Ecosystem Studies (Netherlands), 
2006
2009
€ 2,755,090
https://cordis.europa.eu/project/id/22644/it
CAFÉ - Capacity, F and Effort was a 42 month project designed to investigate the links between fleet capacity, the fishing effort of those fleets and the fishing mortality (F) that results from that effort. Capacity and effort can be seen as linked directly because the effort can be considered as the amount of time a given fishing capacity has been deployed in the fishery. Therefore, engine power in kilowatts could be seen as a capacity measure, and kilowatt hours as the expression of the effort from that capacity. It is often assumed that more capacity and/or more effort will lead to higher fish mortality. However, the existence of capacity does not necessarily predicate the deployment of effort. Many of the fleets examined in CAFÉ did not necessarily expend all or even most of their available "effort". Equally, more effort may not directly result in more fish mortality. So, essentially, the project was designed to test the hypothesis that there was a quantifiable relationship between the capacity and effort by particular fleets and the fishing mortality imposed on the various commercial stocks. A second key component to the project was not to make assumptions about the key variables for describing capacity. Conventionally, and in most management regimes, capacity is taken as some combination of the vessel size (tonnes and length) and its engine power. However there may be many factors that contribute to the fishing power of a vessel beyond these simple size metrics, e.g. sea-keeping ability, the presence of factory systems, and especially the fishers' behaviour. The aim of the CAFÉ project was to identify meaningful measures of capacity with definable and robust links to effort via utilization. Ideally these identified capacity and effort metrics could then be used for a more appropriate management strategy. This approach links to the general definition given by the European Commission for fishing effort as "the sum of means deployed for catching fish in a defined area over a defined period of time". To achieve this end, the project examined six different case studies including the North Sea, the Bay of Biscay, and the east and west Mediterranean. It also considered both pelagic and demersal fisheries, and single and multi-species fisheries. The approach involved firstly assembling a harmonised database on all the fisheries to be considered and over a long enough historical period to determine trends. Much of the data needed for this study was considered confidential or covered by data protection legislation by the partner countries. For this reason, CAFÉ did not attempt to construct a single overall database for all the partners. Instead, we defined a data structure, including all desirable variables and data sets, which each of the partners could assemble. This allowed promising analytical approaches to be easily transferred between partners without taking on the onerous task of assembling a single database. Another key element of the project was that, again, we made no assumptions about the analytical approaches to take. One of the early tasks in the project was to produce a document detailing as wide a range of appropriate analytical techniques as possible, and identifying the expertise in those within the consortium. Partners were then free to apply as many of these as they felt necessary to quantify the capacity, effort and fishing mortality relationships in their study area. As a result, the analyses deployed a wide range of statistical approaches, including; Generalised linear and additive models, Neural networks, Data Envelopment Analysis, Random Utility Models and others. While many of the choices of technique were based on the skills available within each member of the consortium, the project allowed a considerable amount of intellectual transfer, with experts in one field assisting other partners to explore new approaches. The use of Data Envelopment Analysis (DEA) by many of the fishery groups is a good case in point. The analysis carried out followed a logical progression through the project. Once the databases were assimilated and in a common format, the first step was a basic exploratory description of the fleets, their capacity and effort metrics and the fishing mortality linked to them. On the basis of this, the next step was to carry out initial analyses of the data sets to define a set of metrics that could provide the best links between capacity, effort and mortality for the selected case studies. Historically, both effort and capacity metrics have tended to be chosen independently, often based on convenience or availability. The aim here was to choose variables, from a wide range, which provide good relationships between capacity and effort, and between effort and mortality. The modelling work to determine the appropriate metrics explored many of the different techniques available. As well as determining the appropriate metrics, this step also allowed the analysts to focus on the methods that worked best in the context of each specific case study and fleet. In the next stage of the research work, we then used the combination of appropriate models and metrics to quantify the links between capacity (now defined from the earlier modelling work), effort (again based on the choice of metric) and the catch or fishing mortality. Essentially this stage of the research was aimed at analysing the behaviour of the fishery in terms of how the available capacity and associated effort were deployed in actual fishing. The approach was to identify what other external factors determined the choices made by fishers; these included biological factors such as the state of the stocks, economic factors (e.g. prices of fish), and management measures (e.g. TACs or closures). The end product here was as full an understanding as possible of meaningful capacity and effort and what drove the choices made by fishers in the deployment of that. The modelling approach used both statistical modelling techniques (i.e. data driven or empirical approaches) and mathematical modelling techniques (i.e. where an a-priori understanding of the fishers behaviour was translated into a set of mathematical equations expected to describe the system dynamics). The final step was then to use the models and the understanding gained through them to examine the response of the system to a range of management measures for controlling capacity and effort. The aim here was basically to run a series of simulations within the model environments to determine how the fishery might react to limitations on capacity (e.g. decommissioning), effort (e.g. days at sea reductions) or other measures (e.g. a discard ban). In the initial concept of the project, this step was envisioned as being carried out within the, now standard, Management Strategy Evaluation approached using FLR. However, many of the modelling approaches taken in CAFÉ were suitable for use in this simulation mode, so the MSE approach was taken for only some of the case studies. The end product was then the expected results, based on the models, of a range of management measures across the various case study fisheries. As detailed above, in general, the research work was targeted on the data available from one partner country at a time, due to data access problems. However, once the model systems were fully developed, it became possible to deploy these using data from more than one country/fleet combination, while retaining data confidentiality. Several of the models were therefore run again using this multinational data to test the general applicability of the approaches and observe what differences arose between individual countries. In addition to the analysis sequence detailed above, the project also examined historical approaches to capacity and effort management worldwide, and looked at the basis for investment in capacity by a number of the fleets covered by some of the case studies. The historical review of capacity and effort management was intended to provide the context for the various simulations run using the models, and the outcomes from these. It was also intended to serve as a useful reference to the state of play in this key management area. The analysis of investment was intended to provide an understanding of what drove the development of the capacity of the fleets. The basic CAFÉ approach simply took the capacity that was present as a given, and conducted the analysis using that information. This part of the project set out to determine why that capacity was there in the first place, and why fishermen had chosen to buy, sell or upgrade their vessels. CAFE aims to carry out a comprehensive study of capacity and effort metrics and model their relationships to fishing mortality, and the economic drivers for capacity development.
Fisheries management; Fishing effort;
Aegean Sea (GSA 22) Central North Sea (27.IVb) Bay of Biscay Central (27.VIIIb)
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