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

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.