A simulation-based approach to assess sensitivity and robustness of fisheries management indicators for the pelagic fishery in the Bay of Biscay

• Published in: Canadian journal of fisheries and aquatic sciences 2013-12, Vol.70 (12), p.1741-1756
• Main Authors: LEHUTA, Sigrid, MAHEVAS, Stéphanie, LE FLOC'H, Pascal, PETITGAS, Pierre
• Format: Article
• Language: English
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Applied ecology; Biological and medical sciences; Dicentrarchus labrax; Exploitation and management of natural biological resources (hunting, fishing and exploited populations survey, etc.); Fish industry; Fish populations; Fisheries; Fisheries management; Fundamental and applied biological sciences. Psychology; Human beings; Humanities and Social Sciences; Influence on nature; Management; Marine; Performance management; Sardina pilchardus; Sea water ecosystems; Sensitivity analysis; Simulation; Synecology; Thunnus alalunga
• ISSN: 0706-652X; 1205-7533
• DOI: 10.1139/cjfas-2013-0066

Indicators are widely promoted as means to monitor ecosystem status or to evaluate fisheries management performance. “Which indicators are most relevant as decision-support tools in fisheries management?” still remains a topical question. Indicators should be metrics related to fish populations and fleets and should be sensitive to management strategies. However, given the complexity of the processes involved, it is often difficult to unequivocally interpret variations in metrics. A simulation approach was used to study metric properties and to identify robust and relevant fishery indicators. By applying sensitivity analysis methods, simulation designs were built that cross a variety of management scenarios and uncertainty hypotheses. Bio-economic outputs were simulated using a mechanistic model (ISIS-Fish), and their properties were statistically analyzed. This approach was applied to the pelagic fishery of the Bay of Biscay. The analysis of metric properties highlighted the major factors driving variations in each metric and identified the important sources of uncertainty that need to be reduced to allow the use of metrics as indicators. Although very few metrics gave robust indications of management performance, sensitivity indices evidenced how management performances could be improved, and spatially disaggregated metrics provided insights into the mechanisms underlying management performance.