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Statistical principles for ecological status classification of Water Framework Directive monitoring data
Carstensen, J. (2007). Statistical principles for ecological status classification of Water Framework Directive monitoring data. Mar. Pollut. Bull. 55(1-6): 3-15. https://dx.doi.org/10.1016/j.marpolbul.2006.08.016
In: Marine Pollution Bulletin. Macmillan: London. ISSN 0025-326X; e-ISSN 1879-3363, meer
Ook verschenen in:
Devlin, M.; Best, M.; Haynes, D. (Ed.) (2007). Implementation of the Water Framework Directive in European marine waters. Marine Pollution Bulletin, 55(Spec. Issue 1-6). Elsevier: Amsterdam. 297 pp., meer
Peer reviewed article  
Beschikbaar in  Auteur 
Trefwoorden
    Aquatic communities > Plankton > Phytoplankton
    Classification
    Collections > Data collections
    Costs
    Environments > Aquatic environment > Marine environment
    Indicators
    Models > Mathematical models
    Pollution > Water pollution
    Pollution > Water pollution > Marine pollution
    Temporal variations > Periodic variations > Seasonal variations
    Marien/Kust
Author keywords
    confidence; covariation; nutrients; phytoplankton; seasonality;statistical power
Auteur  Top 
  • Carstensen, J.
Abstract
    Bias, precision and confidence of the classification framework are crucial elements for decisions to invest large sums to improve the ecological quality. In this study, the statistical principles for classification in relation to WFD are outlined and exemplified. Indicator adjustment to seasonal variation and other significant covariates reduces bias and improves precision. Precision is generally improved using annual means with seasonal adjustment instead of seasonal means. For classification I argue that the balance between costs of monitoring and reduction measures is only fully maintained by the fail-safe approach. The required monitoring efforts to ensure a precise classification are substantially higher than envisaged in WFD, for nutrients and phytoplankton measurements as high as 500 observations to characterise a water body. It must be ensured that sufficient monitoring data become available for classification, while indicator bias and precision is improved through modelling and further development of measurement techniques.
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