Coastal Zone Ecosystem Services: From science to values and decision making; a case study

This research is concerned with the following environmental research questions: socio-ecological system complexity, especially when valuing ecosystem services; ecosystems stock and services flow sustainability and valuation; the incorporation of scale issues when valuing ecosystem services; and the integration of knowledge from diverse disciplines for governance and decision making. In this case study, we focused on ecosystem services that can be jointly supplied but independently valued in economic terms: healthy climate (via carbon sequestration and storage), food (via fisheries production in nursery grounds), and nature recreation (nature watching and enjoyment). We also explored the issue of ecosystem stock and services flow, and we provide recommendations on how to value stock and flows of ecosystem services via accounting and economic values respectively. We considered broadly comparable estuarine systems located on the English North Sea coast: the Blackwater estuary and the Humber estuary. In the past, these two estuaries have undergone major land-claim. Managed realignment is a policy through which previously claimed intertidal habitats are recreated allowing the enhancement of the ecosystem services provided by saltmarshes. In this context, we investigated ecosystem service values, through biophysical estimates and welfare value estimates. Using an optimistic (extended conservation of coastal ecosystems) and a pessimistic (loss of coastal ecosystems because of, for example, European policy reversal) scenario, we find that context dependency, and hence value transfer possibilities, vary among ecosystem services and benefits. As a result, careful consideration in the use and application of value transfer, both in biophysical estimates and welfare value estimates, is advocated to supply reliable information for policy making.

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.