Combining sensitivity and uncertainty analysis to evaluate the impact of management measures with ISIS–Fish: marine protected areas for the Bay of Biscay anchovy (Engraulis encrasicolus) fishery

Abstract

Lehuta, S., Mahévas, S., Petitgas, P., and Pelletier, D. 2010. Combining sensitivity and uncertainty analysis to evaluate the impact of management measures with ISIS–Fish: marine protected areas for the Bay of Biscay anchovy (Engraulis encrasicolus) fishery. – ICES Journal of Marine Science, 67: 1063–1075. Spatio-seasonal explicit simulation models can predict the impact of spatial management measures on marine fish populations and fishing activities. As fisheries are complex systems, fisheries simulation models are often complex, with many uncertain parameters. Here, the methodology is provided to deliver fishery diagnostics within an uncertainty context using a complex simulation tool. A sensitivity analysis of the model is performed on model outputs using partial least-squares to identify the most sensitive parameters. The impact of several management measures is then simulated using a statistical simulation design taking into account the uncertainty of the selected sensitive parameters. This approach was applied to the Bay of Biscay anchovy stock using the ISIS-Fish (Integration of Spatial Information for Simulation of Fisheries) model to assess the impact of imposing marine protected areas (MPAs) conditionally on parameter uncertainty. The diagnostic appeared to be highly sensitive to the mortality of larvae and juveniles, growth, and reproduction. The uncertainty of the values of these parameters did not permit any of the simulated MPA designs to be proposed. However, according to anchovy catch and biomass, the simulations allowed the low impact of closure duration to be shown and underscored the utility of protecting such key processes as spawning.

Integrated ecological–economic fisheries models—Evaluation, review and challenges for implementation

“…12 ISIS-FISH Mahevas & Pelletier, 2004;Pelletier et al, 2009;Drouineau, Mahévas, Pelletier, & Beliaeff, 2006;Drouineau, Mahévas, Bertignac, & Duplisea, 2010;Duplisea, 2010;Lehuta, Mahévas, Petitgas, & Pelletier, 2010;Rocklin, Pelletier, Mouillot, Tomasini, & Culioli, 2010;Lehuta, Mahévas, & Le Floc'h, 2013;Lehuta, Petitgas, et al, 2013;Lehuta, Holland, & Pershing, 2014;Lehuta, Vermard, & Marchal, 2015;Rochet & Rice, 2010;Marchal, Little, & Thebaud, 2011;Marchal, De Oliveira, Lorance, Baulier, & Pawlowski, 2013;Hussein et al, 2011a,b;Vermard et al, 2012;Gasche, Mahevas, & Marchal, 2013;Reecht et al, 2015. 13 BALTIC FLR-SMS Bastardie et al, 2009;Bastardie, Nielsen, & Kraus, 2010;Bastardie, Vinther, Nielsen, Ulrich, & Storr-Paulsen, 2010;Bastardie, Vinther, & Nielsen, 2012;Bastardie, Nielsen, & Vinther, 2015;Nielsen et al, 2011;Feekings et al, (submitted). Macher, Guyader, Talidec, & Bertignac, 2008;Macher et al, 2013;Merzéréaud, Biais, Lissardy, Bertignac, & Biseau, 2013;Merzéréaud et al, 2011;Simmonds et al, 2011;Raveau et al, 2012;Guillén et al, 2013;Guillén, Macher, Merzéréaud, Fifas, & Guyader, 2014;Guillén, Macher, Merzéréaud, Boncoeur, & Guyader, 2015;EU STECF, 2015a…”

Assessing the impact of artisanal and recreational fishing and protection on a white seabream (Diplodus sargus sargus) population in the north-western Mediterranean Sea using a simulation model. Part 1: Parameterization and simulations

“…However, predicting such impact is not easy because fisheries are complex systems with many interactions at various spatial and temporal scales (Lehuta et al, 2010). Consequently, complex spatially explicit models that incorporate MPAs are required.…”

“…In Pelletier et al (2009), version 3.0 of ISIS-Fish is presented, which encompasses new developments in the software and underlying model, such as a bio-economic model of fisheries dynamics. Recently, this simulation tool has been applied to a number of case studies in several North Atlantic European fisheries: the French Gulf of Biscay Nephrops norvegicus-Merluccius merluccius (Drouineau et al, 2006;Pelletier et al, 2007), the eastern Baltic Sea cod (Gadus morhua callarius) (Kraus et al, 2008) and the Bay of Biscay anchovy (Engraulis encrasicolus) fisheries (Lehuta et al, 2010).…”

Modelling the spatio-temporal distribution of age-1 Bay of Biscay anchovy (<i>Engraulis encrasicolus</i>) at spawning time

“…If the management goal is to maximize the first spawning, it could be useful to close coastal areas in spring (Lehuta et al 2010). However, if there are low age-1 population levels, and especially when the newly recruited individuals form only a small fraction of the population, it is essential to also protect the areas where the older individuals are more abundant in order to maintain the overall level of the spawning biomass and also rebuild the contingent structure of the population.…”