Package {mumarinex}

Title: Computation of the Multivariate Marine Recovery Index
Version: 2.0
Description: Computation of the multivariate marine recovery index, including functions for data visualization and ecological diagnostics of marine ecosystems. The computational details are described in the original publication. Reference: Chauvel, N., Grall, J., Thiébaut, E., Houbin, C., Pezy, J.-P., 2026. A general-purpose multivariate marine recovery index (MUMARINEX) for quantifying the influence of human activities on benthic habitat ecological status. Ecological Indicators 188, 115002.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 8.0.0
Suggests: rmarkdown
VignetteBuilder: knitr
Imports: knitr, vegan, utils
Collate: 'Simulated_data.R' 'decomplot.R' 'mumarinex.R' 'diagnostic_tool.R' 'mum_GUI.R'
URL: https://github.com/Nathan-Chauvel/mumarinex
BugReports: https://github.com/Nathan-Chauvel/mumarinex/issues
NeedsCompilation: no
Packaged: 2026-06-04 20:37:55 UTC; Natha
Author: Nathan Chauvel ORCID iD [aut, cre]
Maintainer: Nathan Chauvel <nathan.chauvel@outlook.fr>
Depends: R (≥ 3.5.0)
Repository: CRAN
Date/Publication: 2026-06-05 06:20:12 UTC

Simulated_data

Description

This dataset was constructed to simulate the theoretical impacts of a disturbance on the abundances of 12 hypothetical species. Reference stations REF1 and REF2 represent control sites, with alternating taxa showing normally distributed abundances (means of 500 and 50, standard deviation of 10% of mean). Stations RI, RD and RDI illustrate changes in community richness, represented by the loss of species C and D (station RD), the appearance of species E and F (station RI), or both simultaneously (station RDI). Stations AD, AI and ADI represent changes in community structure, expressed as a decrease in the abundance of species G and H (station AD), an increase in the abundance of species I and J (station AI), or both (station ADI). Station D focuses on community dominance shifts, characterized by a strong increase in the abundance of species K and L. Station M1 combines all of these effects (loss, gain, structural shifts, and dominance changes). Species A and B are insensitive to impacts and act as tolerant taxa.

Usage

data(Simulated_data)

Format

A data frame with 50 rows (samples) and 12 species:

Sp_A

Species tolerant to different impacts, mean abundance = 500

Sp_B

Species tolerant to different impacts, mean abundance = 50

Sp_C

Species highly sensitive to impacts at stations RD, RDI, and M, leading to their disappearance, mean abundance = 500

Sp_D

Species highly sensitive to impacts at stations RD, RDI, and M, leading to their disappearance, mean abundance = 50

Sp_E

Species favored by impacts at stations RI, RDI, and M, leading to their appearance, mean abundance = 500

Sp_F

Species favored by impacts at stations RI, RDI, and M, leading to their appearance, mean abundance = 50

Sp_G

Species sensitive to impacts at stations AD, ADI, and M, leading to decreased abundance, mean abundance = 1000

Sp_H

Species sensitive to impacts at stations AD, ADI, and M, leading to decreased abundance, mean abundance = 500

Sp_I

Species stimulated by impacts at stations AI, ADI, and M, leading to increased abundance, mean abundance = 1000

Sp_J

Species stimulated by impacts at stations AI, ADI, and M, leading to increased abundance, mean abundance = 500

Sp_K

Species strongly stimulated by impacts at stations D and M, leading to a shift in dominance, mean abundance = 1000

Sp_L

Species strongly stimulated by impacts at stations D and M, leading to a shift in dominance, mean abundance = 500

Author(s)

N. Chauvel

Decomposition of the MUMARINEX value into its three sub-indices and representation as boxplots

Description

Generates a graphical representation (boxplot) of MUMARINEX sub-indices to assess which component(s) contribute most strongly to the overall MUMARINEX score.

Usage

decomplot(
  x,
  g,
  ref,
  log = TRUE,
  fill = c("lightblue", "#FFFFE0DF", "#90F0909E"),
  border = c("#0080AB", "#C7C700DF", "#0091009E"),
  main = NULL
)

Arguments

x

A data frame organized with samples in rows and species in columns.

g

A vector of length nrow(x) indicating how the samples should be grouped (e.g., stations, treatments).

ref

A logical or numeric vector identifying the reference row positions.

log

A logical indicating whether the data must be log transformed.

fill

Fill color of the boxplots.

border

Border color of the boxplots.

main

Main title of the plot.

Details

Decomposition of the MUMARINEX value into its three sub-indices and representation as boxplots

Value

A boxplot of length g representing the variations in the different MUMARINEX sub-indices (SCSR, CBCS, and SPI).

See Also

diagnostic_tool()

Examples

data("Simulated_data")
ref_idx<-1:10
stations<-matrix(unlist(strsplit(rownames(Simulated_data),".",fixed=TRUE)),ncol=2,byrow=TRUE)[,1]
decomplot(x=Simulated_data,g=stations,ref=ref_idx,log=FALSE,main="Simulated data")

Diagnostic tool to identify the key components that best explain the MUMARINEX sub-indices

Description

Identifies, for each sub-index, the species or taxa that contribute most to its variation.

Usage

diagnostic_tool(x, g, ref, log = TRUE, signif_test = TRUE, mute = FALSE)

Arguments

x

A data frame or a matrix organized with samples in rows and species in columns.

g

A vector of length nrow(x) indicating how the samples should be grouped (e.g., stations, treatments).

ref

A logical or numeric vector identifying the reference row positions.

log

A logical indicating whether the data must be log transformed.

signif_test

Logical; if TRUE, only sub-indices significantly lower than the reference conditions (t-test, p < 0.05) are returned. Conditions that are not statistically significant are indicated by 'ns'.

mute

A logical indicating whether the results are displayed in the console.

Details

diagnostic_tool

Value

A data frame summarizing the key information explaining variations in SCSR, CBCS, and SPI.

Note

To reduce the risk of misleading interpretations, a one-sided significance test (t-test) is applied to sub-indices against the reference condition. Nevertheless, taxa identified as contributing to sub-index and MUMARINEX variations may not always be ecologically relevant, and the results are provided without any guarantee. This tool is not a substitute for a thorough ecological knowledge of the studied site and careful examination of the data, although it may help guide users toward potential hypotheses. The significance tests can be disabled, but the resulting outputs should then be interpreted with extreme caution.

See Also

decomplot()

Examples

data("Simulated_data")
ref_idx<-1:10
stations<-matrix(unlist(strsplit(rownames(Simulated_data),".",fixed=TRUE)),ncol=2,byrow=TRUE)[,1]
diagnostic_tool(x=Simulated_data,g=stations,ref=ref_idx,log=FALSE)

Online Graphical User Interface to compute MUMARINEX

Description

Launch the online Graphical User Interface to compute the MUMARINEX index and its sub-indices.

Usage

mum_GUI()

Details

mum_GUI

Value

Opens the web browser to the online Shiny application.

Examples

mum_GUI()

MUMARINEX and subindices computation

Description

Computes the MUMARINEX index and its sub-indices (SCSR, CBCS, and SPI), following the method described in Chauvel et al. (2026).

Usage

mumarinex(x, ref, subindices = FALSE, log = TRUE)

Arguments

x

A data frame or a matrix organized with samples in rows and species in columns.

ref

A logical or numeric vector identifying the reference row positions.

subindices

A logical indicating whether the sub-indices (SCSR, CBCS, and SPI) should be returned.

log

A logical indicating whether the data must be log transformed.

Details

mumarinex

Value

A data frame with the MUMARINEX values. When subindices = TRUE, an additional data frame is returned containing the results of the sub-indices (SCSR, CBCS, and SPI).

References

Chauvel, N., Pezy, J.P., Grall, J., Thiébaut, E. (2026). A general-purpose Multivariate Marine Recovery Index for quantifying the influence of human activities on benthic habitat ecological status. Ecological Indicator.

Examples

data("Simulated_data")
ref_idx<-1:10
mumarinex(x=Simulated_data,ref=ref_idx,log=FALSE)
mumarinex(x=Simulated_data,ref=ref_idx,subindices=TRUE,log=FALSE)