Calculates DKI (v1), the Danish Quality Index

DKI() calculates the original version of the Danish quality index DKI (Carstensen et al., 2014)

The DKI is based on AMBI and can only be calculated after first calculating AMBI, the AZTI Marine Biotic Index, and H', the Shannon diversity index. Both indices are included in output from the function AMBI().

The function uses an estimated maximum possible value of H' in Danish waters (H_max) as a reference value to normalise DKI. If this value is not specified as an argument, the default value is used (5.0) (Borja et al., 2007)

Usage

DKI(AMBI, H, N, S, H_max = 5)

Arguments

AMBI: AMBI, the AZTI Marine Biotic Index, calculated using AMBI()
H: H', the Shannon diversity index, calculated using Hdash()
N: number of individuals - returned by both AMBI() and Hdash()
S: number of species - returned by both AMBI() and Hdash()
H_max: maximum H' used to normalise AMBI, default 5

Value

DKI index value

Details

The AMBI() and Hdash() functions take a dataframe of observations as an argument. The DKI functions, DKI2() and DKI(), do not take a dataframe as an argument. Instead they take values of the input parameters, either single values or as vectors.

To calculate DKI for a dataframe of AMBI values, it could be called from e.g. within a dplyr::mutate() function call. See the examples below.

References

Borja, A., Josefson, A., Miles, A., Muxika, I., Olsgard, F., Phillips, G., Rodriguez, J., Rygg, B. (2007). An Approach to the Intercalibration of Benthic Ecological Status Assessment in the North Atlantic Ecoregion, According to the European Water Framework Directive. Marine Pollution Bulletin, 55(1-6), 42-52. doi:10.1016/j.marpolbul.2006.08.018 .

Carstensen, J., Krause-Jensen, D., Josefson, A. (2014). "Development and testing of tools for intercalibration of phytoplankton, macrovegetation and benthic fauna in Danish coastal areas." Aarhus University, DCE – Danish Centre for Environment and Energy, 85 pp. Scientific Report from DCE – Danish Centre for Environment and Energy No. 93. https://dce2.au.dk/pub/SR93.pdf

Examples


# Simple example

DKI(AMBI = 1.61, H = 2.36, N = 25, S = 6)
#> [1] 0.55683


# ------ Example workflow for calculating DKI from species counts ----

# calculate AMBI index
df <- AMBI(test_data_DK, by = c("station"), var_rep="sample")[["AMBI"]]
#> ℹ 7 species names were not recognised:
#> 1. Bivalvia indet.
#> 2. Enteropneusta indet.
#> 3. Gastropoda indet.
#> 4. Holothuroidea indet.
#> 5. Hydrozoa indet.
#> 6. Nemertini indet.
#> 7. Terebellida indet.

# modify names which were not recognized by AMBI() and recalculate
df_obs <- dplyr::mutate(test_data_DK, species = gsub(" indet\\.", "", species))
df <- ambiR::AMBI(df_obs, by = c("station"), var_rep="sample")[["AMBI"]]
#> ℹ 4 species names were not recognised:
#> 1. Bivalvia
#> 2. Gastropoda
#> 3. Nemertini
#> 4. Terebellida

# show AMBI results
df
#> # A tibble: 2 × 13
#>   station  AMBI AMBI_SD     H     S     fNA     N     I    II   III     IV
#>   <chr>   <dbl>   <dbl> <dbl> <int>   <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
#> 1 42      0.970   0.712  4.24    28 0          76 0.461 0.158 0.355 0.0132
#> 2 49      1.98    0.232  3.25    29 0.00337   297 0.115 0.470 0.389 0.0270
#> # ℹ 2 more variables: V <dbl>, Disturbance <chr>

# calculate DKI from AMBI results
df <- dplyr::mutate(df, DKI = DKI(AMBI, H, N, S))

dplyr::select(df, station, AMBI, H, N, S, DKI)
#> # A tibble: 2 × 6
#>   station  AMBI     H     N     S   DKI
#>   <chr>   <dbl> <dbl> <dbl> <int> <dbl>
#> 1 42      0.970  4.24    76    28 0.834
#> 2 49      1.98   3.25   297    29 0.671