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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

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Beschikbaar in	Auteur
VLIZ: Non-open access 157371 [ aanvragen ]

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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

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Auteur		Top
Carstensen, J.

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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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