A regime shift in the North Sea circa 1988 linked to changes in the North Sea horse mackerel fishery

After 1987, Phytoplankton Colour (a visual estimate of chlorophyll) measured on samples taken by the continuous plankton recorder (CPR) in the North Sea increased substantially, both in level and seasonal extent, compared to earlier years since 1946. Many species of phytoplankton and zooplankton showed marked changes in abundance at about the same time. These events coincided with a large increase in catches of the western stock of the horse mackerel (Trachurus trachurus L.) in the northern North Sea reflecting a northerly expansion of the stock along the shelf edge from the Bay of Biscay to the North Sea after 1987. Using a 3D hydrodynamic model, with input from measured wind parameters, monthly transport of oceanic water into the North Sea has been calculated for the period 1976–1994, integrated for a section from Orkney to Shetland to Norway. A substantial increase in oceanic inflow occurred in the winter months, December to March, from 1988. Higher sea surface temperatures were also measured after 1987 especially in spring and summer months. These biological and physical events may be a response to observed changes in pressure distribution over the North Atlantic. From 1988 onwards, the North Atlantic Oscillation (NAO) index, the pressure difference between Iceland and the Azores, increased to the highest positive level observed in this century. Positive NAO anomalies are associated with stronger and more southerly tracks of the westerly winds and higher temperatures in western Europe. These changing wind distributions may have led to an increase in the northerly advection of water along the western edge of the European shelf and may have assisted the migration of the horse mackerel. This study is possibly a unique demonstration of a correlation between three different trophic levels of a marine ecosystem and hydrographic and atmospheric events at decadal and regional scales. The results emphasise the importance of maintaining into the future long term programmes such as the CPR.

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.