| Title: | Computation of the Multivariate Marine Recovery Index |
|---|---|
| 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. |
| Authors: | Nathan Chauvel [aut, cre] (ORCID: <https://orcid.org/0000-0002-8464-9944>) |
| Maintainer: | Nathan Chauvel <[email protected]> |
| License: | GPL-3 |
| Version: | 2.0 |
| Built: | 2026-06-03 21:51:17 UTC |
| Source: | https://github.com/nathan-chauvel/mumarinex |
Generates a graphical representation (boxplot) of MUMARINEX sub-indices to assess which component(s) contribute most strongly to the overall MUMARINEX score.
decomplot( x, g, ref, log = TRUE, fill = c("lightblue", "#FFFFE0DF", "#90F0909E"), border = c("#0080AB", "#C7C700DF", "#0091009E"), main = NULL )decomplot( x, g, ref, log = TRUE, fill = c("lightblue", "#FFFFE0DF", "#90F0909E"), border = c("#0080AB", "#C7C700DF", "#0091009E"), main = NULL )
x |
A data frame organized with samples in rows and species in columns. |
g |
A vector of length |
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. |
Decomposition of the MUMARINEX value into its three sub-indices and representation as boxplots
A boxplot of length g representing the variations in the different MUMARINEX sub-indices (SCSR, CBCS, and SPI).
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")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")
Identifies, for each sub-index, the species or taxa that contribute most to its variation.
diagnostic_tool(x, g, ref, log = TRUE, signif_test = TRUE, mute = FALSE)diagnostic_tool(x, g, ref, log = TRUE, signif_test = TRUE, mute = FALSE)
x |
A data frame or a matrix organized with samples in rows and species in columns. |
g |
A vector of length |
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. |
diagnostic_tool
A data frame summarizing the key information explaining variations in SCSR, CBCS, and SPI.
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.
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)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)
Launch the online Graphical User Interface to compute the MUMARINEX index and its sub-indices.
mum_GUI()mum_GUI()
mum_GUI
Opens the web browser to the online Shiny application.
mum_GUI()mum_GUI()
Computes the MUMARINEX index and its sub-indices (SCSR, CBCS, and SPI), following the method described in Chauvel et al. (2026).
mumarinex(x, ref, subindices = FALSE, log = TRUE)mumarinex(x, ref, subindices = FALSE, log = TRUE)
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. |
mumarinex
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).
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.
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)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)
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.
data(Simulated_data)data(Simulated_data)
A data frame with 50 rows (samples) and 12 species:
Species tolerant to different impacts, mean abundance = 500
Species tolerant to different impacts, mean abundance = 50
Species highly sensitive to impacts at stations RD, RDI, and M, leading to their disappearance, mean abundance = 500
Species highly sensitive to impacts at stations RD, RDI, and M, leading to their disappearance, mean abundance = 50
Species favored by impacts at stations RI, RDI, and M, leading to their appearance, mean abundance = 500
Species favored by impacts at stations RI, RDI, and M, leading to their appearance, mean abundance = 50
Species sensitive to impacts at stations AD, ADI, and M, leading to decreased abundance, mean abundance = 1000
Species sensitive to impacts at stations AD, ADI, and M, leading to decreased abundance, mean abundance = 500
Species stimulated by impacts at stations AI, ADI, and M, leading to increased abundance, mean abundance = 1000
Species stimulated by impacts at stations AI, ADI, and M, leading to increased abundance, mean abundance = 500
Species strongly stimulated by impacts at stations D and M, leading to a shift in dominance, mean abundance = 1000
Species strongly stimulated by impacts at stations D and M, leading to a shift in dominance, mean abundance = 500
N. Chauvel