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.

M-AMBI revisited: looking inside a widely-used benthic index

Sigovini;Marco;Keppel;Erica;Tagliapietra;Davide
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.
2013
Istituto di Scienze Marine - ISMAR
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/185680
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