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A fish-based index of estuarine ecological quality incorporating information from both scientific fish survey and experts knowledge
Tableau, A.; Drouineau, H.; Delpech, C.; Pierre, M.; Lobry, J.; Le Pape, O.; Breine, J.; Lepage, M. (2013). A fish-based index of estuarine ecological quality incorporating information from both scientific fish survey and experts knowledge. Ecol. Indic. 32: 147-156.
In: Ecological Indicators. Elsevier: Shannon. ISSN 1470-160X; e-ISSN 1872-7034, more
Peer reviewed article  

Available in  Authors 
    VLIZ: Open Repository 257409 [ OMA ]

    Marine; Brackish water
Author keywords
    Anthropogenic pressure; Bayesian method; Expert judgement; Multimetric fish-based indicator; Prior information; Water Framework Directive

Authors  Top 
  • Tableau, A.
  • Drouineau, H.
  • Delpech, C.
  • Pierre, M.
  • Lobry, J.
  • Le Pape, O.
  • Breine, J., more
  • Lepage, M.

    In the Water Framework Directive (European Union) context, a multimetric fish based index is required to assess the ecological status of French estuarine water bodies. A first indicator called ELFI was developed, however similarly to most indicators, the method to combine the core metrics was rather subjective and this indicator does not provide uncertainty assessment. Recently, a Bayesian method to build indicators was developed and appeared relevant to select metrics sensitive to global anthropogenic pressure, to combine them objectively in an index and to provide a measure of uncertainty around the diagnostic. Moreover, the Bayesian framework is especially well adapted to integrate knowledge and information not included in surveys data. In this context, the present study used this Bayesian method to build a multimetric fish based index of ecological quality accounting for experts knowledge. The first step consisted in elaborating a questionnaire to collect assessments from different experts then in building relevant priors to summarize those assessments for each water body. Then, these priors were combined with surveys data in the index to complement the diagnosis of quality. Finally, a comparison between diagnoses using only fish data and using both information sources underlined experts knowledge contribution. Regarding the results, 68% of the diagnosis matched demonstrating that including experts knowledge thanks to the Bayesian framework confirmed or slightly modified the diagnosis provided by survey data but influenced uncertainty around the diagnostic and appeared especially relevant in terms of risk management.

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