Summary
New work to improve the protocol for calculation of supporting boundaries for good ecological status
Setting appropriate parameter boundaries that support ecological status is a vital step in the protection and restoration of aquatic ecosystems. It enables programmes of measures to be tailored to maintain or restore waterbodies on a type specific basis in cases where parameters such as nutrients are driving status. Previous work made significant improvements in harmonising the approach to nutrient boundary setting in the EU, introducing several methods and producing values for MS (Phillips et al. 2018). However, some limitations were also present in the methods and data used, with some estimated boundary values not corresponding to MS expert’s estimations. In addition, only nutrients were considered and not other parameters representing a gap in knowledge which was subsequently judged by ECOSTAT as needing additional work. In response to the ECOSTAT requests, further work is ongoing to develop boundary setting approaches for other physico-chemical elements as well as improvements in the methods for setting evidence-based thresholds, with particular reference to nutrients. This report presents a new protocol and the results of its application to calculate nutrient boundaries in separate documents for lakes, rivers and TRAC. It is intended to extend the method, where appropriate, to other parameters.
About
Best Practice Guide on establishing nutrient concentrations to support good ecological status
Geoff Phillips, Martyn Kelly, Wera Leujak, Fuensanta Salas, Heliana Teixeira, Anne Lyche Solheim, Gary Free, Gabor Varbiro
Final version following testing at Bucharest workshop November 2018
Summary
- 1. High concentrations of inorganic nutrients are a major factor contributing to the failure of many water bodies to achieve Good Ecological Status and Member States need to determine levels appropriate to their own territories.
- 2. This report describes statistical methods for determining appropriate concentrations for supporting ecological status. These statistical methods need to be set in a broader framework that also encompasses chemical, ecological and regulatory aspects relevant to the type of water body under consideration.
- 3. Three approaches to setting these threshold concentrations are included. These are: • Regression analysis, using a continuous relationship between EQR and nutrient concentration • Categorical analysis, using the distribution of nutrient concentration within biological classes • Minimisation of mis-match of classifications for biology and nutrients
- 4. The choice of method depends upon a number of factors, including the length of the gradient that available datasets cover and the statistical strength of the relationship between the explanatory and response variables. In some cases, Member States may be better able to achieve the statistical prerequisites for methods by joining forces with neighbours who share similar water body types.
- 5. Excel and R-based statistical “toolkits” are provided in order to make calculation of threshold concentrations more straightforward.
- 6. Options for situations where none of these methods are appropriate are also described.
- 7. Finally, some practical issues associated with the use of these threshold concentrations for regulation are discussed.
Disclaimer
This application has been developed through a collaborative framework (the Common Implementation Strategy (CIS)) involving the Member States, EFTA countries, and other stakeholders including the European Commission. The document is a working draft and does not necessarily represent the official, formal position of any of the partners.
To the extent that the European Commission's services provided input to this technical document, such input does not necessarily reflect the views of the European Commission.
Neither the European Commission nor any other CIS partners are responsible for the use that any third party might make of the information contained in this document.
The technical document is intended to facilitate the implementation of Directive 2000/60/EC and is not legally binding. Any authoritative reading of the law should only be derived from Directive 2000/60/EC itself and other applicable legal texts or principles. Only the Court of Justice of the European Union is competent to authoritatively interpret Union legislation.
For support contact: varbirog@gmail.com
Reference
Please reference the use of this application in any publications as
Phillips, G., Teixeira, H., Kelly, M., Lyche Solheim, A., Free, G., Salas Herrero, M.F., Kolada, A., Varbiro, G., Poikane, S.: Establishing supporting element standards. A revised approach and applications, European Commission: Joint Research Centre, Publications Office of the European Union, 2024, https://data.europa.eu/doi/10.2760/55461Import
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Content of the datafile
Stressor Boundary Tester
How to use the boundary checker
This panel checks whether a selected stressor boundary is consistent with the biological Good/Moderate boundary. Move the stressor and EQR sliders to test candidate values; the plots and confusion-matrix metrics update immediately.
- Biota class: records at or above the selected EQR boundary are treated as good or better; records below it are treated as moderate or worse.
- Stressor class: records below the selected stressor boundary are normally treated as supporting good status. Tick reversed trend only when higher stressor values indicate better condition.
- Decision focus: look for low omission and commission errors, acceptable kappa, and a biologically plausible boundary inside the observed data range.
Select if the stressor trend is reversed:
Variable selection: choose one numeric stressor and one numeric biological metric. Before interpreting the diagnostics, confirm that units, decimal marks and missing values are correct. If all records import into one column, change the separator; if numbers import as text, check the decimal mark.
Select the stressor or supporting element to test. The slider defines the candidate stressor boundary used to classify records as good or moderate.
Select the biological response metric, usually an EQR or BQE metric. The Good-Moderate slider defines the biological reference classification.
Select the Good-Moderate boundary for the biological metric
Figure 1. Confusion matrix map
This plot translates the selected EQR and stressor thresholds into four agreement/error zones. Green and blue zones are correct classifications; orange and red zones are mismatches. Larger coloured areas indicate a larger share of records in that cell.
Figure 2. EQR-stressor boundary plot
Points show the observed relationship between the biological metric and the selected stressor. Dashed lines mark the candidate Good-Moderate EQR boundary and stressor boundary. The smooth line is a descriptive trend, not a final decision rule.
Figure 3. Omission versus commission
This plot compares false-negative and false-positive error rates. Prefer candidate boundaries near the lower-left corner. The diagonal lines help judge whether omission and commission are balanced; colour indicates kappa agreement category.
Metrics to inspect
- kappa: agreement beyond chance; >0.4 is useful, >0.6 is strong.
- Omission: good biological sites classified as below good by the stressor boundary.
- Commission: below-good biological sites classified as good by the stressor boundary.
- Sensitivity/Specificity: ability to detect good and below-good records.
- CCR: overall correct classification rate; check together with prevalence.
- Prevalence: balance of good versus below-good biological records.
Confusion matrix output
Use TP, TN, FP and FN to understand the type of mismatch. A boundary can have a good overall classification rate but still be unsuitable if errors are strongly unbalanced or ecologically implausible.