Assessing Ecological Status of Transitional and Coastal Waters; Current Difficulties and Alternative Approaches

The environmental monitoring strategy termed ecosystem-based approach (EBA) underlines the obvious benefits of managing natural resources on a holistic level, and it is particularly invoked for the rational and sustainable management of aquatic resources. However, when coming to implement EBA into monitoring schemes, such as those derived from the implementation of the European legislation concerning water quality, difficulties inherent to the complex and dynamic nature of ecosystems arise, including (i) identify appropriate, relevant and easily measurable indicators of ecosystem integrity, and (ii) combine the heterogeneous information gathered at the different levels of organization included in an ecosystem into a simple and practical decision-making scheme. The first kind of difficulties maybe partially overcome by implementing monitoring schemes which take into account the hierarchical nature of ecosystem processes and did not neglect the use of indicators at low levels of biological organization, including ecotoxicological biomarkers and bioassays. Secondly, the integration of the monitoring results into a practical decision-making scheme can best be achieved by using non-metric multivariate analysis, which is especially suitable for data bases including different metrics, and allows the processing of variables showing non-monotonic response to human stress, from molecular biomarkers to community indices. The difficulties inherent to the current rigid scheme of water quality assessment heavily based on ratio-to-reference univariate indicators and arbitrary reference values and class boundaries for each single indicator are illustrated with a case study in the Minho estuary (NW Iberian Peninsula). The classification of aquatic ecosystems into discrete categories of ecological status can best be achieved by combining observations at different levels of biological organization, from molecular biomarkers to community traits, with explicative physicochemical and hydromorphological elements, and by using non-metric multivariate analysis techniques.