Implementing AZTI’s Marine Biotic Index (Borja et al., 2000)
Overview
The ambiR package allows the user to calculate both the AMBI index and the multivariate M-AMBI from count or abundance data for benthic fauna species, using the included species list.
In addition, this package includes functions to calculate different versions of the Danish Quality Index (DKI) (Dansk Kvalitetsindeks), a derived benthic index based on AMBI.
-
AMBI()- calculates the AMBI index. -
MAMBI()- calculates the multivariate M-AMBI index. -
Hdash()- calculates H’, the Shannon diversity index. -
DKI2()- calculates the salinity-normalised Danish quality index DKI (v2). -
DKI()- calculates the Danish quality index DKI.
To get started, see vignette("ambiR"). For details about running in interactive mode, see vignette("interactive").
The AMBI index was developed by Ángel Borja and colleagues at AZTI. For background and explanation of the method for calculation of the AMBI index see vignette("background"). You can also find a link to download the original AMBI software.
Installation
Install from CRAN with:
install.packages("ambiR")You can install the development version of ambiR from GitHub with:
# install.packages("remotes)
remotes::install_github("NIVA-Denmark/ambiR")or, if you prefer to use devtools:
# install.packages("devtools")
devtools::install_github("NIVA-Denmark/ambiR")Examples
This is a basic example using a very small dataset, with 2 stations. In each station there are 3 species counts. The data is organised in a dataframe with 3 columns in a so-called “long” format. That is, each count is recorded in a separate row.
By default, the AMBI function returns both the AMBI results and a list of which AMBI groups species names in input data were assigned to. Here, we also use the argument format_pct to show the estimated fractions as percentages. See the function documentation for more details: AMBI().
library(ambiR)
df <- data.frame(station = c("1","1","1","2","2","2"),
species = c("Acidostoma neglectum",
"Acrocirrus validus",
"Capitella nonatoi",
"Acteocina bullata",
"Austrohelice crassa",
"Capitella nonatoi"),
count = c(8, 14, 23, 13, 17, 11))
AMBI(df, by = c("station"), format_pct=1)
#> $AMBI
#> # A tibble: 2 × 12
#> station AMBI H S fNA N I II III IV V
#> <chr> <dbl> <dbl> <int> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
#> 1 1 5.43 1.46 3 17.8% 45 0.0% 0.0% 0.0% 37.8% 62.2%
#> 2 2 4.10 1.56 3 0.0% 41 31.7% 0.0% 0.0% 0.0% 68.3%
#> # ℹ 1 more variable: Disturbance <chr>
#>
#> $matched
#> station species species_matched count group RA
#> 1 1 Acidostoma neglectum Acidostoma neglectum 8 0 0
#> 2 1 Acrocirrus validus Acrocirrus validus 14 4 0
#> 3 1 Capitella nonatoi Capitella nonatoi 23 5 0
#> 4 2 Acteocina bullata Acteocina bullata 13 1 0
#> 5 2 Austrohelice crassa Austrohelice crassa 17 5 0
#> 6 2 Capitella nonatoi Capitella nonatoi 11 5 0Another example using the supplied test_data.
library(ambiR)
## calling AMBI using the test data set
AMBI(test_data, by=c("station"), var_rep = "replicate", format_pct=1)
#> $AMBI
#> # A tibble: 3 × 13
#> station AMBI AMBI_SD H S fNA N I II III IV V
#> <dbl> <dbl> <dbl> <dbl> <int> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
#> 1 1 1.48 0.338 1.80 6 0.0% 16 12.5% 75.0% 12.5% 0.0% 0.0%
#> 2 2 1.89 0.238 3.54 22 0.0% 80 40.0% 13.8% 30.0% 15.0% 1.2%
#> 3 3 4.12 0.884 2.50 9 0.0% 24 0.0% 12.5% 29.2% 8.3% 50.0%
#> # ℹ 1 more variable: Disturbance <chr>
#>
#> $AMBI_rep
#> # A tibble: 8 × 11
#> station replicate AMBI S fNA N I II III IV V
#> <dbl> <chr> <dbl> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
#> 1 1 a 1.8 3 0.0% 5 0.0% 80.0% 20.0% 0.0% 0.0%
#> 2 1 b 1.5 3 0.0% 7 14.3% 71.4% 14.3% 0.0% 0.0%
#> 3 1 c 1.12 2 0.0% 4 25.0% 75.0% 0.0% 0.0% 0.0%
#> 4 2 a 1.88 12 0.0% 32 40.6% 15.6% 21.9% 21.9% 0.0%
#> 5 2 b 2.13 12 0.0% 19 31.6% 15.8% 36.8% 10.5% 5.3%
#> 6 2 c 1.66 10 0.0% 29 44.8% 10.3% 34.5% 10.3% 0.0%
#> 7 3 a 3.5 5 0.0% 6 0.0% 33.3% 16.7% 33.3% 16.7%
#> 8 3 b 4.75 6 0.0% 18 0.0% 5.6% 33.3% 0.0% 61.1%
#>
#> $matched
#> # A tibble: 53 × 7
#> station replicate species species_matched count group RA
#> <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 1 a Cumopsis fagei Cumopsis fagei 2 2 0
#> 2 1 a Diogenes pugilator Diogenes pugilator 2 2 0
#> 3 1 a Paradoneis armata Paradoneis armata 1 3 0
#> 4 1 b Bathyporeia elegans Bathyporeia elegans 1 1 0
#> 5 1 b Diogenes pugilator Diogenes pugilator 5 2 0
#> 6 1 b Dispio uncinata Dispio uncinata 1 3 0
#> 7 1 c Astarte sp. Astarte sp. 1 1 0
#> 8 1 c Diogenes pugilator Diogenes pugilator 3 2 0
#> 9 2 a Cumopsis fagei Cumopsis fagei 1 2 0
#> 10 2 a Glycera tridactyla Glycera tridactyla 2 2 0
#> # ℹ 43 more rowsAcknowledgements
The artwork for the ambiR logo was created by Steen Knudsen.
Development work on the ambiR package is funded in part by the GES4SEAS project.
Grant Agreement 101059877 - GES4SEAS
The GES4SEAS project has been approved under the HORIZON-CL6-2021-BIODIV-01-04 call: ‘Assess and predict integrated impacts of cumulative direct and indirect stressors on coastal and marine biodiversity, ecosystems and their services’.
Funded by the European Union. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or UK Research and Innovation. Neither the European Union nor the granting authority can be held responsible for them.