Estuaries, rias, fjords, coastal lagoons, bahiras, river mouths, tidal creeks, deltas and similar coastal environments are often regarded as a single broad conceptual class. ‘Brackish’, ‘estuarine’, ‘paralic’ and ‘transitional’ are terms used in different contexts to designate collectively this class of environments. Nevertheless every term, generated from different historical perspectives and scientific points of view, excludes some of the above-mentioned environments. These terms and definitions were examined with regard to their meaning and history. The main attributes have been extracted from definitions and arranged in a conceptual scheme giving an overall direct perception of their relationships. This analysis provided evidence for the occurrence of two major groups of attributes: hydrological and geomorphic. Although the significance of hydrological attributes is instantly recognisable, geomorphic attributes imply subjacent concepts of geographical scale and hydrological features not expressly formulated in definitions, such as a limited supply of seawater to the system.

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.