The Water Framework Directive (WFD) recognizes benthic macroinvertebrates as a good biological quality element for transitional waters as they are the most exposed to natural variability patterns characteristic of these ecosystems, due to their life cycles and space-use behavior. Here, we address the ecological status classification issue for three lagoons in Apulia, using benthic macroinvertebrates and three proposed multimetric indices (namely M-AMBI, BITS and ISS), likely to respond differently to different sources of stress and natural variability. Lagoon classification is based on discretization by standard classification boundaries with only partial consideration of the natural variability of ecosystem properties and possible inaccuracies of the classification procedures. In order to investigate the possible contrasting behavior of the three classifications, we propose Bayesian hierarchical models in which the multimetric indices and their discrete counterparts are jointly modeled as function of abiotic covariates, external anthropogenic pressures indicators and spatio-temporal effects.

Bayesian analysis of the multivariate dependence of three transition water ecosystem classifications

ARIMA, SERENA;
2013

Abstract

The Water Framework Directive (WFD) recognizes benthic macroinvertebrates as a good biological quality element for transitional waters as they are the most exposed to natural variability patterns characteristic of these ecosystems, due to their life cycles and space-use behavior. Here, we address the ecological status classification issue for three lagoons in Apulia, using benthic macroinvertebrates and three proposed multimetric indices (namely M-AMBI, BITS and ISS), likely to respond differently to different sources of stress and natural variability. Lagoon classification is based on discretization by standard classification boundaries with only partial consideration of the natural variability of ecosystem properties and possible inaccuracies of the classification procedures. In order to investigate the possible contrasting behavior of the three classifications, we propose Bayesian hierarchical models in which the multimetric indices and their discrete counterparts are jointly modeled as function of abiotic covariates, external anthropogenic pressures indicators and spatio-temporal effects.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11587/472103
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