Abstract
A huge variability exists in nutrient concentrations boundaries set for the Water (WFD) and the Marine Strategy (MSFD) Framework Directives, as revealed by a survey to EU Member States. Such wide variation poses challenges when checking policy objectives compliance and for setting coherent management goals across European waters. To help Member States achieve Good Ecological Status (GES) in surface waters, different statistical approaches have been proposed in a Best Practice Guide (CIS Nutrients Standards Guidance) for establishing suitable nutrient boundaries. Here we used the intercalibrated results from the WFD for the biological quality element phytoplankton to test the applicability of this Best Practice Guide for deriving nutrient boundaries in coastal and transitional waters. Overall, the statistical approaches proved adequate for coastal lagoons, but are not always robust to allow deriving nutrient boundaries in other water categories such as estuaries, in transitional waters, or some coastal water types. The datasets available for analysis provided good examples of the most common problems that might be encountered in these water categories. Similar issues have been found in freshwater environments, for which solutions are proposed in the Best Practice Guide and which are demonstrated here for coastal and transitional waters. The different approaches available and problems identified can be useful for supporting the derivation of nutrient concentrations boundaries both for the Water (WFD) and the Marine Strategy (MSFD) Framework Directives implementation.
Highlights
European water policy aims to achieve good ecological status (GES) for all rivers, lakes, coastal and transitional water bodies of European Union (European Commission [EC], 2000)
The toolkit provides the full R code, together with a series of examples which can be used to explore the methods. This toolkit includes different statistical approaches to derive nutrient boundaries: Univariate linear regression: assuming a linear relationship between the ecological quality ratio (EQR) and nutrients, three regression types are implemented: two ordinary least squares OLS linear regressions between EQR and log nutrients concentration, where each variable is alternatively treated like the independent variable; and a third, type II regression, the ranged major axis (RMA) regression
To keep the examples comparable, in this study we have focused the analysis in the phytoplankton response to total Nitrogen (TN), total Phosphorus (TP) and dissolved inorganic Nitrogen (DIN) parameters, since these are the most commonly applied across geographic intercalibration groups (GIG) and common types in both water categories (Table 2)
Summary
European water policy aims to achieve good ecological status (GES) for all rivers, lakes, coastal and transitional water bodies of European Union (European Commission [EC], 2000). Transitional and coastal waters (CWs) are among the most highly impacted ecosystems in the world presenting inherently high variability over both spatial and temporal scales (Paerl, 2006; Reyjol et al, 2014). In those environments, the greatest impacts of increasing nutrient concentrations have been observed at sites with restricted water exchange, resulting in phytoplankton and macroalgal blooms (Tett et al, 2003; Salas et al, 2008; Carstensen and Henriksen, 2009; Teichberg et al, 2010)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
