Channel Catchments Cluster. Recent developments in tools and techniques for water quality management in the France (Channel) England region

The Channel Catchments Cluster (3C) aims to capitalise on outputs from some of the recent projects funded through the INTERREG IVa France (Channel) England programme. The river catchment basins draining into the Channel region drain an area of 137,000km2 and support a human population of over 19M. Throughout history, these catchments, rivers and estuaries have been centres of habitation, developed through commerce and industry, providing transport links to hinterland areas. These catchments also provide drinking water and food through provision of agriculture, fisheries and aquaculture. In addition, many parts of the region are also economically important now for the tourism and leisure industries. Consequently, there is a need to manage the balance of these many and varied human activities within the catchments, rivers, estuaries and marine areas to ensure that they are maintained or restored to good environmental condition . This document highlights some of the recent work carried out by projects within the INTERREG IVa programme that provide tools and techniques to assist in the achievement of these goals.

Channel Catchments Cluster: Compendium of Tools

Although the narrow stretch of water that separates England from France has seen both welcome (and occasionally less welcome) exchanges during the past thousand years, this physical challenge to the movement of people has certainly served to obstruct collaborative efforts that might establish a more sustainable economy in our part of Europe. Since 2009, the European Regional Development Fund (ERDF)-supported Interreg Programme France (Channel) England Region has actively supported efforts by organisations in France and England to work ever more closely together, to share good practice and to devise new ways to support sustainable development in the Region. The initiatives are certainly rooted in excellent research, but they have also been driven by the real needs of the Region and in all cases partners have worked to develop practical tools that can be readily applied in both France and England. The Channel Catchment Cluster (3C) builds on this growing tradition of cross-border cooperation to bring together the very best new knowledge from recent Anglo-French teamwork. The contents of this Compendium are the result of a wide variety of grass-roots initiatives that have benefitted enormously from a cross-border meeting of minds. The Cluster has brought together several of these cross-border teams to discuss their work and to share good practice in the dissemination and application of novel tools for environmental protection. This Compendium therefore not only presents a 'snapshot' of the wide variety of environmental protection and management tools that have emerged from the France (Channel) England Region, it also summarises where they stand on their 'pathway to impact'. There is clearly much more that can be achieved by future cross-border efforts in our Region, but I believe that this Compendium provides an excellent basis for future action. Professor Huw Taylor University of Brighton UK

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.