Calculates NQI1, the Norwegian Quality Index

NQI1() calculates the Norwegian Quality Index, NQI1 (Rygg, 2006)

The NQI1 is based on AMBI and can only be calculated after first calculating AMBI, the AZTI Marine Biotic Index, using output from the function AMBI().

NQI1 is the only Norwegian benthic fauna index which is inter-calibrated with all countries belonging to the North East Atlantic Geographical Intercalibration Group (NEAGIG)(Borgersen et al, 2019) .

Usage

NQI1(AMBI, N, S)

Arguments

AMBI: AMBI, the AZTI Marine Biotic Index, calculated using AMBI()
N: number of individuals - returned by both AMBI() and Hdash()
S: number of species - returned by both AMBI() and Hdash()

Value

NQI1 index value

Details

While AMBI() takes a dataframe of observations as an argument, NQI1() does not. Instead, similarly to the DKI functions, it takes values of the input parameters, either single values or as vectors.

To calculate NQI1 for a dataframe of AMBI values, it could be called from e.g. within a dplyr::mutate() function call. See the examples below.

References

Rygg, B. (2006) Developing indices for quality-status classification of marine soft-bottom fauna in Norway. (Report No. 5208) Norwegian Institute for Water Research (NIVA). 33 pp. https://hdl.handle.net/11250/213219 Borgersen, G., Trannum, H.C., Gundersen, H., Vedal, J. (2019). Oppdatering av bløtbunnsartenes sensitivitetsverdier (Report No. 7366). Norsk institutt for vannforskning (NIVA). https://hdl.handle.net/11250/2600903

Examples


# Simple example

NQI1(AMBI = 1.61, N = 25, S = 6)
#> [1] 0.6215256


# ------ Example workflow for calculating NQI1 from species counts ----

# calculate AMBI index using Norwegian example data

df <- AMBI(test_data_NO,
               by = "station",
               var_rep = "sample",
               var_species = "species",
               var_count = "count",
               quiet = TRUE)[["AMBI"]]

# 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 Ærøy (U10)     1.26  0.154   4.87    92 0.0388   645 0.348 0.535 0.0677 0.0452
#> 2 Ærøydypet (U…  2.29  0.0209  4.69    57 0.0345   261 0.111 0.425 0.294  0.171 
#> # ℹ 2 more variables: V <dbl>, Disturbance <chr>

# calculate NQI1 from AMBI results
df <- dplyr::mutate(df, NQI1 = NQI1(AMBI, N, S))
dplyr::select(df, AMBI, N, S, NQI1)
#> # A tibble: 2 × 4
#>    AMBI     N     S  NQI1
#>   <dbl> <dbl> <int> <dbl>
#> 1  1.26   645    92 0.855
#> 2  2.29   261    57 0.764