Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

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.

Ocean Virtual Laboratory: A new way to explore multi-sensor synergy demonstrated over the Agulhas region

Ocean Virtual Laboratory is an ESA-funded project to prototype the concept of a single point of access for all satellite remote-sensing data with ancillary model output and in situ measurements for a given region. The idea is to provide easy access for the non-specialist to both data and state-of-the-art processing techniques and enable their easy analysis and display. The project, led by OceanDataLab, is being trialled in the region of the Agulhas Current, as it contains signals of strong contrast (due to very energetic upper ocean dynamics) and special SAR data acquisitions have been recorded there. The project also encourages the take up of Earth Observation data by developing training material to help those not in large scientific or governmental organizations make the best use of what data are available. The website for access is: http://ovl-project.oceandatalab.com/