Implementing AZTI’s Marine Biotic Index (Borja et al., 2000)
You are currently viewing documentation for the development version of ambiR. Find the documentation for the release version here.
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.
AMBI() - calculates the AMBI index.MAMBI() - calculates the multivariate M-AMBI index.Hdash() - calculates H’, the Shannon diversity index.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.
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")
install.packages("ambiR", repos = c('https://niva-denmark.r-universe.dev', 'https://cloud.r-project.org'))
Documentation for the current development version can be found here.
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 0
Another 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 rows
See vignette("other-indices") for more information and examples
demonstrating other index calculations.
As well as the AMBI index itself, this package provides functions to calculate other benthic indices derived from AMBI. This includes two versions of the Danish Quality Index (DKI) and the Norwegian Quality Index (NQI1):
DKI2() - calculates the salinity-normalised Danish quality index
DKI (v2).DKI() - calculates the original version of the Danish quality index
DKI.NQI1() - calculates the Norwegian quality index NQI1.Functions are also available to calculate the Hurlbert diversity index (ES) and two other Norwegian benthic indices:
ES() - calculates the Hurlbert diversity index ES.NSI() - calculates the Norwegian Sensitivity Index NSI.ISI() - calculates the Indicator Species Index ISI.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.