Data di Pubblicazione:
2013
Abstract:
M-AMBI is a multimetric index for
assessing the ecological quality status of marine and
transitional waters. It is based on benthic macroinvertebrates and integrates AMBI, a biotic index based on
species sensitivity/tolerance, with diversity and richness, making it compliant with the European Water
Framework Directive. The success of AMBI paved the
way for the introduction of M-AMBI, which was
subsequently incorporated into the regulations of
several European countries. The M-AMBI algorithm
integrates the metrics by means of factor analysis
(FA). In this paper, we first reproduced the algorithm
using the open source R software. This enabled us to
point out that FA is not functional to M-AMBI, and its
omission does not appreciably change the results. We
then enhanced the applicability of the index, making it
independent of the number of samples. In this way,
M-AMBI is closely approximated by the simple mean
of the normalised metrics with no need for multivariate techniques. Finally, we further simplified the
approach, presenting a bivariate version that is still
highly correlated with M-AMBI, in which the constitutive metrics are reduced to a diversity measure and a
species sensitivity index. The properties of this
bivariate version include simplicity, transparency,
robustness, and openness.
assessing the ecological quality status of marine and
transitional waters. It is based on benthic macroinvertebrates and integrates AMBI, a biotic index based on
species sensitivity/tolerance, with diversity and richness, making it compliant with the European Water
Framework Directive. The success of AMBI paved the
way for the introduction of M-AMBI, which was
subsequently incorporated into the regulations of
several European countries. The M-AMBI algorithm
integrates the metrics by means of factor analysis
(FA). In this paper, we first reproduced the algorithm
using the open source R software. This enabled us to
point out that FA is not functional to M-AMBI, and its
omission does not appreciably change the results. We
then enhanced the applicability of the index, making it
independent of the number of samples. In this way,
M-AMBI is closely approximated by the simple mean
of the normalised metrics with no need for multivariate techniques. Finally, we further simplified the
approach, presenting a bivariate version that is still
highly correlated with M-AMBI, in which the constitutive metrics are reduced to a diversity measure and a
species sensitivity index. The properties of this
bivariate version include simplicity, transparency,
robustness, and openness.
Tipologia CRIS:
01.01 Articolo in rivista
Elenco autori:
Keppel, Erica; Sigovini, Marco; Tagliapietra, Davide
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