# The AMBI index

The basis of the AMBI index is that soft-bottom macrofauna are divided into groups according to their sensitivity to increasing environmental stress. The distribution of counts of individuals or relative abundance between the different groups is used to calculate a quantitative measure of the ecological quality of the benthic environment.

### Species Groups

Input to the `AMBI()` function is a dataframe of species counts with optional grouping variables, e.g. station or replicate IDs. The function matches species names in the input data with names in the AMBI species list, in order to categorise the observed species according to the AMBI method. The tool then calculates the `AMBI` index resulting from the distribution of individuals between the groups.

The AMBI species list gives the groups (I, II, III, IV, V) in which each species is classified, as described by Borja, Franco, and Pérez (2000).

- Group I
  Species very sensitive to organic enrichment and present under unpolluted conditions (initial state). They include the specialist carnivores and some deposit- feeding tubicolous polychaetes.
- Group II
  Species indifferent to enrichment, always present in low densities with non-significant variations with time (from initial state, to slight unbalance). These include suspension feeders, less selective carnivores and scavengers.
- Group III
  Species tolerant to excess organic matter enrichment. These species may occur under normal conditions, but their populations are stimulated by organic enrichment (slight unbalance situations). They are surface deposit-feeding species, as tubicolous spionids.
- Group IV
  Second-order opportunistic species (slight to pronounced unbalanced situations). Mainly small sized polychaetes: subsurface deposit-feeders, such as cirratulids.
- Group V
  First-order opportunistic species (pronounced unbalanced situations). These are deposit- feeders, which proliferate in reduced sediments.

The list of species and their groups has been updated several times by the authors of the AMBI software. The version of the list used here is from 8. October 2024.

After calculating the fractions \(f_i\) of all individuals belonging to each group \(i \in{} \{I, II, III, IV, V\}\), then the index is given by:

\[ AMBI = 0.0 * f_{I} + 1.5 * f_{II} + 3 * f_{III} + 4.5 * f_{IV} + 6 * f_{V} \]

So, the greater the proportion of sensitive species, the lower the resulting AMBI index. A sample consisting 100% of species from the most sensitive category (Group I) will have an AMBI index of 0.0. A population consisting entirely of species from Group V will have an index of 6.0.

## M-AMBI - the multivariate AMBI index

`MAMBI()` calculates M-AMBI the multivariate AMBI index, based on the three separate species diversity metrics:

- AMBI index AMBI
- Shannon Wiener diversity index H’
- Species richness S

The principles of the M-AMBI index are described by Muxika, Borja, and Bald (2007)

“AMBI, richness and diversity, combined with the use, in a further development, of factor analysis together with discriminant analysis, is presented as an objective tool (named here M-AMBI) in assessing ecological quality status”

It is, of course, possible to calculate M-AMBI using data generated in other analyses, outside the ambiR package but the `AMBI()` function can conveniently provide all 3 of the metrics used as variables in the M-AMBI factorial analysis.

- from the input data with values of AMBI, H’ and S, the variables are first standardized, by subtracting by the mean and then dividing by the standard deviation.

- the analysis requires information on limits for each of the 3 variables: (a) values corresponding to reference or undisturbed conditions. For the Shannon diversity H’ and species richness S, these are taken as the maximum values found in the data. This assumes that some of the observations are from undisturbed sites so care should be given and suitable values provided by the user if this assumption does not hold. For AMBI, the reference condition value used is `0`, unless a different value is specified. (b) default limit values corresponding to bad conditions are `AMBI = 6`, `H = 0` and `S = 0`.

- factor analysis (FA) using the principal component analysis method on the standardized variables generates 3 factors.

- the Varimax rotation method is applied to the results of FA. The factor scores (`x`, `y` and `z`) are the new coordinates of each sampling station in the new factor space.

- These coordinates are used to derive the EQR or M-AMBI values. The M-AMBI score is the mean of the distance along the zero to one scale in the three dimensions. Depending on specific regional conditions the M-AMBI value corresponding to the Good/Moderate and other class boundaries can be used to convert M-AMBI values to a normalised EQR value where the Good/Moderate boundary is at EQR = 0.6.

## AMBI software

The AMBI software was developed as a free standalone software to allow users to perform AMBI index calculations. Later versions were updated to include the multivariate index M-AMBI calculations and adjustments to the species list used to assign species to ecological groups. The software is maintained and updated by AZTI https://www.azti.es, where the latest version can be downloaded.

The ambiR package has been extensively tested and gives identical results to the AMBI software, as long as the version of species list select corresponds to the to the version used by the software.

## References

Borja, Á, J Franco, and V Pérez. 2000. “A Marine Biotic Index to Establish the Ecological Quality of Soft-Bottom Benthos Within European Estuarine and Coastal Environments.” Marine Pollution Bulletin 40 (12): 1100–1114. https://doi.org/10.1016/S0025-326X(00)00061-8.
Muxika, I, Á Borja, and J Bald. 2007. “Using Historical Data, Expert Judgement and Multivariate Analysis in Assessing Reference Conditions and Benthic Ecological Status, According to the European Water Framework Directive.” Marine Pollution Bulletin 55 (1): 16–29. https://doi.org/10.1016/j.marpolbul.2006.05.025.