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Calculates M-AMBI, the multivariate AZTI Marine Biotic Index

Source: R/MAMBI.R
MAMBI.Rd

Calculates M-AMBI the multivariate AMBI index, based on the three separate species diversity metrics:

  • AMBI index AMBI.

  • Shannon diversity index H'

  • Species richness S.

"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" (Muxika et al., 2007)

Usage

MAMBI(
  df,
  by = NULL,
  var_H = "H",
  var_S = "S",
  var_AMBI = "AMBI",
  limits_AMBI = c(bad = 6, high = 0),
  limits_H = c(bad = 0, high = NA),
  limits_S = c(bad = 0, high = NA),
  bounds = c(PB = 0.2, MP = 0.39, GM = 0.53, HG = 0.77)
)

Arguments

df

a dataframe of diversity metrics.

by

a vector of column names found in df by which calculations should be grouped e.g. c("station"). If grouping columns are specified, then the mean values of the 3 metrics will be calculated within each group before calculating M-AMBI (default NULL).

var_H

name of the column in df containing H' Shannon species diversity (default "H").

var_S

name of the column in df containing S species richness (default "S").

var_AMBI

name of the column in df containing AMBI index (default "AMBI").

limits_AMBI

named vector with length 2, specifying the values of AMBI corresponding to (i) worst possible condition ("bad") where M-AMBI and EQR are equal to 0.0 and (ii) the best possible condition ("high") where M-AMBI and EQR are equal to 1.0. Default c("bad" = 6, "high" = 0).

limits_H

named vector with length 2, specifying the values of H' corresponding to (i) worst possible condition ("bad") where M-AMBI and EQR are equal to 0.0 and (ii) the best possible condition ("high") where M-AMBI and EQR are equal to 1.0. Default c("bad" = 0, "high" = NA). If the "bad" value is NA then the lowest value occurring in df and if "high" is NA then the highest value will be used.

limits_S

named vector with length 2, specifying the values of S corresponding to (i) worst possible condition ("bad") where M-AMBI and EQR are equal to 0.0 and (ii) the best possible condition ("high") where M-AMBI and EQR are equal to 1.0. Default c("bad" = 0, "high" = NA). If the "bad" value is NA then the lowest value occurring in df and if "high" is NA then the highest value will be used.

bounds

A named vector (length 4) of EQR boundary values used to normalise M-AMBI to EQR values where the boundary between Good and Moderate ecological status is 0.6. They specify the values of M-AMBI corresponding to the boundaries between (i) Poor and Bad status ("PB"), (ii) Moderate and Poor status ("MP"), (iii) Good and Moderate status ("GM"), and (iv) High and Good status ("HG"). Default c("PB" = 0.2, "MP" = 0.39, "GM" = 0.53, "HG" = 0.77).

Value

a dataframe containing results of the M-AMBI index calculations. For each unique combination of by variables, the following values are calculated:

  • M-AMBI : the M-AMBI index value.

  • x,y,z : factor scores giving coordinates in the new factor space.

If no by variables are specified (by = NULL), then M-AMBI will be calculated for each row in df.

In addition, the dataframe returned contains 2 extra rows. These contain the limits applied for each of the metrics, corresponding to "bad" (M-AMBI = 0.0) and "high" (M-AMBI = 1.0), as specified in the arguments limits_AMBI, limits_H, limits_S or taken from data.

Details

The input dataframe df should contain the three metrics AMBI, H' and S, identified by the column names var_AMBI (default "AMBI"), var_H (default "H") and var_S (default "S").

If any of these three metrics is not found in the input data, then the function will return an error.

AMBI is calculated using the AMBI() function. H' can be calculated using the Hdash() function but it is also included as additional output from AMBI() when called with the non-default argument H = TRUE. S is an output from both functions AMBI() and Hdash().

This means that the input to MAMBI() can be generated from species count data using only using the AMBI() function.

References

Muxika, I., Borja, A., Bald, J. (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, 1–6, doi:10.1016/j.marpolbul.2006.05.025 .

See also

AMBI() which calculates the indices required as input for MAMBI().

Examples


  df <- data.frame(station = c(1, 1, 1, 2, 2, 2, 3, 3),
                 replicates = c("a", "b", "c", "a", "b", "c", "a", "b"),
                 AMBI = c(1.8, 1.5, 1.125, 1.875, 2.133, 1.655, 3.5, 4.75),
                 H = c(1.055, 0.796, 0.562, 2.072, 2.333, 1.789, 1.561, 1.303),
                 S = c(3, 3, 2, 12, 12, 10, 5, 6))

 MAMBI(df, by = c("station"))
#> # A tibble: 5 × 11
#>   station Bounds  AMBI     H     S      x      y        z MAMBI Status   EQR
#>     <dbl> <chr>  <dbl> <dbl> <dbl>  <dbl>  <dbl>    <dbl> <dbl> <chr>  <dbl>
#> 1       1 NA      1.48 0.804  2.67  0.863 -0.248  0.00897 0.464 Mod    0.439
#> 2       2 NA      1.89 2.06  11.3  -2.11  -0.486  0.00359 0.893 High   0.907
#> 3       3 NA      4.12 1.43   5.5   0.346  0.449  0.0334  0.497 Mod    0.456
#> 4      NA Bad     6    0      0     3.53   1.38  -0.0519  0     NA     0    
#> 5      NA High    0    2.06  11.3  -2.63  -1.09   0.00597 1     NA     1