A hierarchical bayesian model for modelling benthic macroinvertebrates densities in lagoons

The ecological status classification of aquatic ecosystems requires separate quantification of natural and anthropogenic sources of environmental variability. A clustering of ecosystems into ecosystem types (i.e. Typology) is used in order to minimise natural variability. Among transitional water quality elements, benthic macroinvertebrates are the most exposed to natural variability patterns due to their life cycles and space-use behavior. Here, we address the ecological status classification issue for Mediterranean and Black Sea lagoons, using benthic macroinvertebrates, from a set of 12 reference lagoons. Two main classification approaches have been proposed in literature: the a-priori approach, based on standard multimetric indices, and the a-posteriori approach, based on linear mixed models. It may happen that different indices are in disagreement with respect to lagoon classification. We propose a Bayesian hierarchical model in which the multimetric indices are jointly modeled through a multivariate normal mixture. Each mixture component is estimated as function of covariates of interest and corresponds to an ecological status. We compare the proposed model with the a-priori and a-posteriori approaches highlighting pros and cons of each method.