Marine Research Findings of the VECTORS Project

This website provides access to the research results of the VECTORS project, which can be used to support marine management decisions, policies and governance as well as future research and investment. VECTORS was a large scale project that brought together more than 200 expert researchers from 16 different countries. It examined the significant changes taking place in European seas, their causes, and the impacts they will have on society.

What are the main drivers of fishers' behaviour? A quantitative approach based on discrete-choice models

Understanding and modelling fleet dynamics and their response to spatial constraints is a prerequisite to anticipating the performance of marine ecosystem management plans. A major challenge for fisheries managers is to be able to anticipate how fishing effort is re-allocated following any permanent or seasonal closure of fishing grounds, given the competition for space with other active maritime sectors. In this study, a Random Utility Model (RUM) was applied to determine how fishing effort is allocated spatially and temporally by a variety of French and Dutch fleets fishing in the eastern English Channel or the southern North Sea. The explanatory variables chosen were past effort i.e. experience or habit, previous catch to represent previous success, species targeting and percentage of area occupied by spatial regulation, or other competing maritime sectors.

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French and Dutch fishers generally adhered to past annual fishing practices. Furthermore, results indicated generally that maritime traffic may impact negatively on fishing decision. Finally, a close correlation was found between predicted and observed re-allocation of fishing effort.

A first Random Utility Model (RUM) was developed and initially applied to determine how fishing effort is allocated spatially and temporally by the French demersal mixed fleet fishing in the Eastern English Channel. The spatial resolution of this investigation was that of an ICES rectangle (30’ x 60’). The explanatory variables chosen were past effort i.e. experience or habit, previous catch to represent previous success, % of area occupied by spatial regulation, and by other competing maritime sectors. Results showed that fishers tended to adhere to past annual fishing practices, except the fleet targeting molluscs which exhibited within year behaviour influenced by seasonality. Furthermore, results indicated generally that maritime traffic may impact negatively on fishing decision. Finally, the model was validated by comparing predicted re-allocation of effort against observed effort, for which there was a close correlation. This method developed was also applied to the Dutch beam trawl fleet (2008-2010). The Dutch fleets’ activity was well captured by the model including only biological and economic drivers. Predictions were accurate and followed the seasonal patterns well. To predict the long term changes in fishing activity additional factors, such as the competition for space with other marine users, should be included and changes in fish distribution should be linked to the current model.

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Another important finding of this study were the limitations associated with the maximum of probability of choosing métier method often used to simulate fisher’s decision based on random utility models. We proposed here a method where an alternative is randomly sampled within the probability distribution derived from the RUM, which improves the predictions.

We used RUMs calibrated over 2007-2008 to forecast French and Dutch trip choices in 2009. In many fisheries applications of discrete-choice models, the forecast choice is taken to be that with the highest probability. However, this approach appears to be rather ad hoc, and the prediction performances of the maximum probability estimator have, to the best of our knowledge, never been contrasted with those of alternative predictors, such as the median of the distribution. In the present case, two methods of prediction were used. With the first method, the choice actually made is assumed to be as in previous studies, the alternative with the highest probability. The second method requires performing 200 simulations. Within each simulation, the choice is randomly selected from a multinomial distribution parameterised by the probability distribution derived from the model calibration. The frequency of each of the alternative choices actually made is then calculated for both methods for each month. For the second method, the median of the 200 frequencies obtained with the random iterations is defined as the frequency of forecasted choices. The test of the two ways to forecast area and métier choice indicated that the median value derived from a random sampling of 200 alternative within the multinomial probability distributions estimated by the RUM best matched the observations. The model fit was always better with the random sampling method than with the method using the maximum of probability as a choice.

Relevance for Policy:
  • Common Fisheries Policy
  • Directive on Maritime Spatial Planning and Integrated Coastal Management (forthcoming)
  • Integrated European Maritime Policy (IMP)
  • Marine Strategy Framework Directive

Lead Author:

Paul Marchal
(paul.manospamrchal@ifremer.fr)
French Research Institute for Exploitation of the Sea (IFREMER)
Date of research: January 2014

Related articles:

Changes on stocks and management in saithe fishery 

Climate change: flatfish and shrimp fisheries 

Connectivity: plaice spawning and nursery areas 

Changes in the upper trophic level: impact on fish

Cod, recruitment, temperature and zooplankton

Deliberative valuation and the Dogger Bank 

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This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 266445
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