Supporting Fisheries Management by Means of Complex Models: Can We Point out Isles of Robustness in a Sea of Uncertainty?

Type Article
Date 2013-10
Language English
Author(s) Gasche Loic1, Mahevas StephanieORCID1, Marchal PaulORCID2
Affiliation(s) 1 : IFREMER Ctr Atlantique, Unite Ecol & Modeles Halieut, Nantes, France.
2 : IFREMER Ctr Manche Mer Nord, Lab Ressources Halieut, Boulogne Sur Mer, France.
Source Plos One (1932-6203) (Public Library Science), 2013-10 , Vol. 8 , N. 10 , P. -
DOI 10.1371/journal.pone.0077566
WOS© Times Cited 10
Abstract Ecosystems are usually complex, nonlinear and strongly influenced by poorly known environmental variables. Among these systems, marine ecosystems have high uncertainties: marine populations in general are known to exhibit large levels of natural variability and the intensity of fishing efforts can change rapidly. These uncertainties are a source of risks that threaten the sustainability of both fish populations and fishing fleets targeting them. Appropriate management measures have to be found in order to reduce these risks and decrease sensitivity to uncertainties. Methods have been developed within decision theory that aim at allowing decision making under severe uncertainty. One of these methods is the information-gap decision theory. The info-gap method has started to permeate ecological modelling, with recent applications to conservation. However, these practical applications have so far been restricted to simple models with analytical solutions. Here we implement a deterministic approach based on decision theory in a complex model of the Eastern English Channel. Using the ISIS-Fish modelling platform, we model populations of sole and plaice in this area. We test a wide range of values for ecosystem, fleet and management parameters. From these simulations, we identify management rules controlling fish harvesting that allow reaching management goals recommended by ICES (International Council for the Exploration of the Sea) working groups while providing the highest robustness to uncertainties on ecosystem parameters.
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