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A fish-based index of estuarine ecological quality incorporating information from both scientific fish survey and experts knowledge

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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, Jan; Lepage, M.

In: Ecological Indicators, Vol. 32, 2013, p. 147-156.

Research output: Contribution to journalA1: Web of Science-article

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Author

Tableau, A. ; Drouineau, H. ; Delpech, C. ; Pierre, M ; Lobry, J. ; Le Pape, O. ; Breine, Jan ; Lepage, M. / A fish-based index of estuarine ecological quality incorporating information from both scientific fish survey and experts knowledge. In: Ecological Indicators. 2013 ; Vol. 32. pp. 147-156.

Bibtex

@article{7814017ec8c94905a2dbef6a42d4dc37,
title = "A fish-based index of estuarine ecological quality incorporating information from both scientific fish survey and experts knowledge",
abstract = "In the Water Framework Directive (European Union) context, a multimetric fish based index is required toassess 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 andthis indicator does not provide uncertainty assessment. Recently, a Bayesian method to build indicatorswas developed and appeared relevant to select metrics sensitive to global anthropogenic pressure, tocombine 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 informationnot included in surveys data. In this context, the present study used this Bayesian method to build a multimetricfish based index of ecological quality accounting for experts knowledge. The first step consisted inelaborating a questionnaire to collect assessments from different experts then in building relevant priorsto summarize those assessments for each water body. Then, these priors were combined with surveysdata in the index to complement the diagnosis of quality. Finally, a comparison between diagnoses usingonly fish data and using both information sources underlined experts knowledge contribution. Regardingthe results, 68{\%} of the diagnosis matched demonstrating that including experts knowledge thanks to theBayesian framework confirmed or slightly modified the diagnosis provided by survey data but influenceduncertainty around the diagnostic and appeared especially relevant in terms of risk management.",
author = "A. Tableau and H. Drouineau and C. Delpech and M Pierre and J. Lobry and {Le Pape}, O. and Jan Breine and M. Lepage",
year = "2013",
doi = "10.1016/j.ecolind.2013.03.030",
language = "English",
volume = "32",
pages = "147--156",
journal = "Ecological Indicators",
issn = "1470-160X",
publisher = "Elsevier Science",

}

RIS

TY - JOUR

T1 - A fish-based index of estuarine ecological quality incorporating information from both scientific fish survey and experts knowledge

AU - Tableau, A.

AU - Drouineau, H.

AU - Delpech, C.

AU - Pierre, M

AU - Lobry, J.

AU - Le Pape, O.

AU - Breine, Jan

AU - Lepage, M.

PY - 2013

Y1 - 2013

N2 - In the Water Framework Directive (European Union) context, a multimetric fish based index is required toassess 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 andthis indicator does not provide uncertainty assessment. Recently, a Bayesian method to build indicatorswas developed and appeared relevant to select metrics sensitive to global anthropogenic pressure, tocombine 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 informationnot included in surveys data. In this context, the present study used this Bayesian method to build a multimetricfish based index of ecological quality accounting for experts knowledge. The first step consisted inelaborating a questionnaire to collect assessments from different experts then in building relevant priorsto summarize those assessments for each water body. Then, these priors were combined with surveysdata in the index to complement the diagnosis of quality. Finally, a comparison between diagnoses usingonly fish data and using both information sources underlined experts knowledge contribution. Regardingthe results, 68% of the diagnosis matched demonstrating that including experts knowledge thanks to theBayesian framework confirmed or slightly modified the diagnosis provided by survey data but influenceduncertainty around the diagnostic and appeared especially relevant in terms of risk management.

AB - In the Water Framework Directive (European Union) context, a multimetric fish based index is required toassess 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 andthis indicator does not provide uncertainty assessment. Recently, a Bayesian method to build indicatorswas developed and appeared relevant to select metrics sensitive to global anthropogenic pressure, tocombine 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 informationnot included in surveys data. In this context, the present study used this Bayesian method to build a multimetricfish based index of ecological quality accounting for experts knowledge. The first step consisted inelaborating a questionnaire to collect assessments from different experts then in building relevant priorsto summarize those assessments for each water body. Then, these priors were combined with surveysdata in the index to complement the diagnosis of quality. Finally, a comparison between diagnoses usingonly fish data and using both information sources underlined experts knowledge contribution. Regardingthe results, 68% of the diagnosis matched demonstrating that including experts knowledge thanks to theBayesian framework confirmed or slightly modified the diagnosis provided by survey data but influenceduncertainty around the diagnostic and appeared especially relevant in terms of risk management.

U2 - 10.1016/j.ecolind.2013.03.030

DO - 10.1016/j.ecolind.2013.03.030

M3 - A1: Web of Science-article

VL - 32

SP - 147

EP - 156

JO - Ecological Indicators

JF - Ecological Indicators

SN - 1470-160X

ER -

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