Data from: Bringing multivariate support to multiscale codependence analysis: assessing the drivers of community structure across spatial scales

1. Multiscale codependence analysis (MCA) quantifies the joint spatial distribution of a pair of variables in order to provide a spatially-explicit assessment of their relationships to one another. For the sake of simplicity, the original definition of MCA only considered a single response variable (e.g. a single species). However, that definition would limit the application of MCA when many response variables are studied jointly, for example when one wants to study the effect of the environment on the spatial organisation of a multi-species community in an explicit manner. 2. In the present paper, we generalize MCA to multiple response variables. We conducted a simulation study to assess the statistical properties (i.e. type I error rate and statistical power) of multivariate MCA (mMCA) and found that it had honest type I error rate and sufficient statistical power for practical purposes, even with modest sample sizes. We also exemplified mMCA by applying it to two ecological data sets. 3. The simulation study confirmed the adequacy of mMCA from a statistical standpoint: it has honest type I error rates and sufficient power to be useful in practice. Using mMCA, we were able to detect variation in fish community structure along the Doubs River (in France), which was associated with large spatial structures in the variation of physical and chemical variables related to water quality. Also, mMCA usefully described the spatial variation of an Oribatid mite community structure associated with a gradient of water content superimposed on various smaller-scale spatial features associated with vegetation cover in the peat blanket surrounding Lac Geai (in Québec, Canada). 4. In addition to demonstrating the soundness of mMCA in theory and practice, we further discuss the strengths and assumptions of mMCA and describe other potential scenarios where it would be helpful to biologists interested in assessing influence of environmental conditions on community structure in a spatially-explicit way.Script performing simulations and examples calculationsThe tarball (.tar.gz) archive contains the files to perform the simulation study outlined in the paper and calculate the application examples. R language code is commented in French and English. It uses MySQL for data management, the R language and environment for calculations, and a bash script to launch the simulation scripts. R package codep is used for MCA calculation and is available from the CRAN (https://cran.r-project.org/web/packages/codep/index.html). The tarball contains a README file explaining the purposes of the enclosed directories and files in addition to the R, SQL, and bash scripts.MS15 - Multi-codep.tar.gzR package codepA copy of the R package codep used to run the scripts. Up-to-date versions are available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/codep/index.htm