Using the MUMARINEX Package
Nathan Chauvel
Introduction
The mumarinex package provides tools for
the computation of the MUltivariate MArine Recovery INdEX
(MUMARINEX) as described in Chauvel et al. (2026).
This index is designed to evaluate community recovery in marine
ecosystems by combining three complementary sub-indices:
- Standardized Corrected Shared Richness (SCSR):
community richness changes (taxa loss or gain)
- Corrected Bray-Curtis Similarity (CBCS): community
structure changes (decrease or increase in abundance)
- Standardized Pielou Index (SPI): community
dominance shifts (appearance or changes in dominance)
The package also includes diagnostic and visualization functions to
identify which taxa or ecological mechanisms drive the observed
variations.
In this vignette, we will observe how to:
- Install and load the package.
- Format data.
- Compute the MUMARINEX index and its sub-indices (SCSR, CBCS and
SPI).
- Visualize sub-indices results with dedicated plotting
functions.
- Generate diagnostics to interpret sub-indices variations.
All examples below use the dataset
Simulated_data, included in the
package.
For details on how this dataset was constructed, please refer to its
documentation page (?Simulated_data)
Install and load the package
You can install the development version of mumarinex from CRAN or
from GitHub, respectively with:
```r load-data, message=FALSE
install.packages(“mumarinex”) # CRAN
devtools::install_github(“Nathan-Chauvel/mumarinex”) # GitHub
Once you run one of this above command, you can load the package with:
``` r
library(mumarinex)
Compute the MUMARINEX index and its sub-indices (SCSR, CBCS and
SPI)
Once the data are properly defined, the MUMARINEX index and its
sub-indices can be computed. The function mumarinex()
calculates the MUMARINEX index, and by setting
subindices = TRUE, it also returns the three complementary
sub-indices.
# Compute MUMARINEX and sub-indices
rMUM <- mumarinex(x = Simulated_data, ref = ref_idx, subindices = TRUE, log = FALSE)
# Extract MUMARINEX
rMUMARINEX<-rMUM$MUMARINEX
# Extract sub-indices
Subind<-rMUM$Subindices
MUMARINEX results can subsequently be examined through graphical
representations, such as boxplots.
stations<-matrix(unlist(strsplit(rownames(Simulated_data),".",fixed=TRUE)),ncol=2,byrow=TRUE)[,1] # get station labels from data rownames
stations<-factor(stations,levels=unique(stations)) # setting station names as factor to specify in which order it must display it in the boxplot
boxplot(rMUMARINEX~stations,ylim=c(0,1)) # ylim is set in the interval 0-1 as it is the range of MUMARINEX
Visualize sub-indices results with dedicated plotting functions
To better understand the variations in MUMARINEX, it is often useful
to examine how the sub-indices vary. The decomplot()
function displays the distribution of these sub-indices (SCSR, CBCS,
SPI) across sample groups using boxplots.
decomplot(x = Simulated_data, g = stations, ref = ref_idx, log = FALSE, main = "Artificial data")
Generate diagnostics to interpret sub-indices variations
Once the sub-index variations underlying the final MUMARINEX value
have been examined, the diagnostic_tool() function can be
used to identify the species that best account for these changes.
diagnostic_tool(x = Simulated_data, g = stations, ref = ref_idx, log = FALSE)
#>
#> |-----------------------------------------------------------------------------------|
#> |--------------------------------- SCSR diagnostic --------------------------------|
#> |-----------------------------------------------------------------------------------|
#> > Raw: Raw taxa difference between sample and reference pool
#> > Mean: Mean taxa difference between sample and reference pool
#> > Missing_species: Top 5 diff_taxa species (sorted by IndVal of the reference)
#> > New_species: Top 5 new species (sorted by IndVal of the sample)
#>
#>
#> Table: SCSR diagnostic
#>
#> Sample Raw Mean Missing_species New_species
#> ------- ------- ------- ----------------------- -----------------------
#> RD ns ns ns ns
#> ------ ------ ------ ---------------------- ----------------------
#> / Sp_E
#> / Sp_F
#> RI 2 2 / /
#> / /
#> / /
#> ------ ------ ------ ---------------------- ----------------------
#> Sp_C Sp_E
#> Sp_D Sp_F
#> RDI 4 4 / /
#> / /
#> / /
#> ------ ------ ------ ---------------------- ----------------------
#> / /
#> / /
#> AD 0 0 / /
#> / /
#> / /
#> ------ ------ ------ ---------------------- ----------------------
#> / /
#> / /
#> AI 0 0 / /
#> / /
#> / /
#> ------ ------ ------ ---------------------- ----------------------
#> ADI ns ns ns ns
#> ------ ------ ------ ---------------------- ----------------------
#> D ns ns ns ns
#> ------ ------ ------ ---------------------- ----------------------
#> M ns ns ns ns
#> ------ ------ ------ ---------------------- ----------------------
#>
#> |-----------------------------------------------------------------------------------|
#> |--------------------------------- CBCS diagnostic ---------------------------------|
#> |-----------------------------------------------------------------------------------|
#> > Lower_abundance: Important reference taxa which present lower abundances
#> > Decrease: Mean decrease (vs reference) of the corresponding taxa
#> > Relative_D: Relative mean decrease (vs reference) of the corresponding taxa (%)
#> > Higher_abundance: Important reference taxa which present higher abundances
#> > Increase: Mean increase (vs reference) of the corresponding taxa
#> > Relative_I: Relative mean increase (vs reference) of the corresponding taxa (%)
#>
#>
#> Sample Lower_abundance Decrease Relative_D Higher_abundance Increase Relative_I
#> ------- ----------------------- --------- ----------- ----------------------- --------- -----------
#> Sp_L -36.1 -7 Sp_B 2.5 5.3
#> Sp_H -16.8 -3.3 Sp_A 20.9 4.2
#> RD Sp_G -5.5 -0.6 Sp_I 16.3 1.6
#> / / / Sp_K 10 1
#> / / / Sp_J 4.2 0.8
#> ------ ---------------------- ------ ------ ---------------------- ------ ------
#> Sp_L -28.9 -10.2 Sp_A 34.1 6.9
#> Sp_I -17.3 -5.6 Sp_B 2.9 6.2
#> RI Sp_H -11.6 -2.3 Sp_C 26.3 5.7
#> Sp_D -5.4 -1.7 Sp_J 2.6 0.5
#> / / / Sp_G 3.3 0.3
#> ------ ---------------------- ------ ------ ---------------------- ------ ------
#> Sp_L -15.5 -3 Sp_B 1.7 3.6
#> Sp_H -10 -2 Sp_K 21.2 2.1
#> RDI Sp_A -8.7 -1.8 Sp_I 10.1 1
#> Sp_J -4.2 -0.8 Sp_G 4.3 0.4
#> / / / / / /
#> ------ ---------------------- ------ ------ ---------------------- ------ ------
#> Sp_G -490.3 -50.6 Sp_C 40.9 8.8
#> Sp_H -256.2 -49.5 Sp_B 3.3 7
#> AD Sp_K -69.6 -16.5 Sp_A 15.9 3.2
#> Sp_L -44.7 -8.6 / / /
#> Sp_D -8.8 -7.1 / / /
#> ------ ---------------------- ------ ------ ---------------------- ------ ------
#> Sp_H -10.4 -8.3 Sp_J 256.4 51.2
#> Sp_K -6.6 -2.1 Sp_I 483.5 48.5
#> AI Sp_D -4.4 -0.7 Sp_B 6.1 13
#> / / / Sp_C 53.5 11.6
#> / / / Sp_L 43.1 8.3
#> ------ ---------------------- ------ ------ ---------------------- ------ ------
#> Sp_G -489.7 -50.8 Sp_I 521.1 52.3
#> Sp_H -257.2 -49.5 Sp_J 249 49.8
#> ADI Sp_A -10.3 -3 Sp_C 52.5 11.3
#> Sp_K -4.4 -2.1 Sp_B 2.9 6.2
#> Sp_D -1.6 -0.4 Sp_L 6.1 1.2
#> ------ ---------------------- ------ ------ ---------------------- ------ ------
#> Sp_A -11.1 -2.3 Sp_K 5344.6 541.5
#> Sp_H -10.6 -2.1 Sp_L 2216.7 428.8
#> D Sp_J -6.4 -1.9 Sp_B 4.7 10
#> Sp_D -1 -1.3 Sp_C 15.7 3.4
#> Sp_I -0.7 -0.1 Sp_G 26.3 2.7
#> ------ ---------------------- ------ ------ ---------------------- ------ ------
#> Sp_G -491.3 -50.5 Sp_K 5292.6 536.2
#> Sp_H -256 -49.6 Sp_L 2567.5 496.7
#> M / / / Sp_I 532.5 53.5
#> / / / Sp_J 249.4 49.8
#> / / / Sp_B 4.9 10.4
#> ------ ---------------------- ------ ------ ---------------------- ------ ------
#>
#> |-----------------------------------------------------------------------------------|
#> |--------------------------------- SPI diagnostic ---------------------------------|
#> |-----------------------------------------------------------------------------------|
#> > Dominant_species: most abundant species (corrected by reference)
#> > Contribution: Taxa contribution (%) to total abundance (corrected by reference)
#>
#>
#> Table: SPI diagnostic
#>
#> Sample Dominant_species Contribution
#> ------- ----------------------- -------------
#> Sp_K 52.9
#> Sp_A 21.9
#> RD Sp_I 15.6
#> Sp_J 3.7
#> Sp_B 3.7
#> ------ ---------------------- ------
#> Sp_E 73.5
#> Sp_F 7.4
#> RI Sp_K 6.4
#> Sp_A 5.6
#> Sp_C 4.1
#> ------ ---------------------- ------
#> RDI ns ns
#> ------ ---------------------- ------
#> Sp_C 50.9
#> Sp_A 31.3
#> AD Sp_I 5.9
#> Sp_J 5.9
#> Sp_B 3.6
#> ------ ---------------------- ------
#> AI ns ns
#> ------ ---------------------- ------
#> ADI ns ns
#> ------ ---------------------- ------
#> Sp_K 69.9
#> Sp_L 29
#> D Sp_G 0.3
#> Sp_C 0.2
#> Sp_A 0.2
#> ------ ---------------------- ------
#> Sp_K 57.2
#> Sp_L 27.8
#> M Sp_I 5.8
#> Sp_E 5.4
#> Sp_J 2.7
#> ------ ---------------------- ------