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The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts. [ABSTRACT FROM AUTHOR]

Abstract: A multi-species approach to fisheries management requires taking into account the interactions between species in order to improve recruitment forecasting of the fish species. Recent advances in Bayesian networks direct the learning of models with several interrelated variables to be forecasted simultaneously. These models are known as multi-dimensional Bayesian network classifiers (MDBNs). Pre-processing steps are critical for the posterior learning of the model in these kinds of domains. Therefore, in the present study, a set of ‘state-of-the-art’ uni-dimensional pre-processing methods, within the categories of missing data imputation, feature discretization and feature subset selection, are adapted to be used with MDBNs. A framework that includes the proposed multi-dimensional supervised pre-processing methods, coupled with a MDBN classifier, is tested with synthetic datasets and the real domain of fish recruitment forecasting. The correctly forecasting of three fish species (anchovy, sardine and hake) simultaneously is doubled (from 17.3% to 29.5%) using the multi-dimensional approach in comparison to mono-species models. The probability assessments also show high improvement reducing the average error (estimated by means of Brier score) from 0.35 to 0.27. Finally, these differences are superior to the forecasting of species by pairs. [Copyright &y& Elsevier]

It is accepted that world’s fisheries are not generally exploited at their biological or their economic optimum. Most fisheries assessments focus on the biological capacity of fish stocks to respond to harvesting and few have attempted to estimate the economic efficiency at which ecosystems are exploited. The latter is important as fisheries contribute considerably to the economic development of many coastal communities. Here we estimate the overall potential economic rent for the fishing industry in the North Atlantic to be B€ 12.85, compared to current estimated profits of B€ 0.63. The difference between the potential and the net profits obtained from North Atlantic fisheries is therefore B€ 12.22. In order to increase the profits of North Atlantic fisheries to a maximum, total fish biomass would have to be rebuilt to 108 Mt (2.4 times more than present) by reducing current total fishing effort by 53%. Stochastic simulations were undertaken to estimate the uncertainty associated with the aggregate bioeconomic model that we use and we estimate the economic loss NA fisheries in a range of 2.5 and 32 billion of euro. We provide economic justification for maintaining or restoring fish stocks to above their MSY biomass levels. Our conclusions are consistent with similar global scale studies. [ABSTRACT FROM AUTHOR]