Data generation and analysis programs used in the simulation study:
--source code and executables for SIMSSD4, a FORTRAN program used for generation of the species, environment, and geographic coordinate data files in the simulation study;
--MATLAB code used to automatically carry out canonical and Mantel variation partitioning, with permutation tests, on the generated data sets.
Ecological Archives M075-017-S1.
Authors
File list (downloads)
Description
Pierre Legendre
Université de Montréal
Case postale 6128, succursale Centre Ville
Montréal, Québec
H3C 3J7
Canada
[email protected]Daniel Borcard
Département de sciences biologiques
Université de Montréal
Case postale 6128, succursale Centre Ville
Montréal, Québec
H3C 3J7
Canada
[email protected]Pedro Peres-Neto
Department of Biology
University of Regina
Regina, Saskatchewan
S4S 0A2
Canada
[email protected]
SimSSD program:
SimSSD4_g77.for: Fortran source code for SimSSD for Macintosh OS X and Windows
SimSSD4 user's guide.pdf: SimSSD user's manual
File_of_parameters.txt: file of parameters for test run
Coord.txt, Species.txt, Envir.txt: results of the test run of SimSSD
All these files, as well as executables for Macintosh OS X and Windows, are available in the compressed files SimSSD_for_OS_X.zip and SimSSD_for_Windows.zip.
MATLAB functions:
Guide.pdf: guide to the 8 MATLAB functions and 5 text files containing the necessary files to run one of the scenarios considered in our study.
MatlabFunctions.zip: contains all the files described in the Guide.pdf document.
SimSSD program:
The zip files for Macintosh OS X or Windows contain the source code and executables for SimSSD4, a FORTRAN program used for generation of the species, environment, and geographic coordinate data files in the simulation study. SimSSD4 is a new version of a simulation program used previously to study the consequences of spatial structures for the design of ecological field surveys and field experiments. Through the use of a conditional sequential Gaussian simulation method, this program allows to generate species data with autocorrelation of known intensity under a particular variogram model. The program also allows an environmental variable to have an influence, called β by reference to a regression coefficient, of known intensity on some of the species. The species also contained random N(0,1) "innovation" at each site.
MATLAB code:
A suite of MATLAB functions used to automatically carry out canonical and Mantel variation partitioning, with permutation tests, on the generated data sets. All functions can be used independently, so that they can be easily applied to any data set of interest. The .zip file also contains data files used in the main paper. The functions are:
DistanceVariationPartition.m: variation partitioning on a distance matrix (or similarity) Y based on two data distance matrices X and W. Tests of significance on fractions are performed by permutation.
eucl.m: Euclidean distances among a set of points, or between a reference point and a set of points, or among all possible pairs of two sets of points, in P dimensions. Returns a single distance for two points. (author: Richard Strauss).
ForwardSelectionRDA.m: forward selection procedure of regressors in RDA. Function follows implementation of ter Braak and Smilauer with the exception that the procedure stops once a regressor is considered non-significant according to the α level established.
HellingerTransformation.m: Hellinger Transformation of species abundance data.
RDATest.m: permutation test for assessing the relationship between two multivariate data matrices Y and X based on redundancy analysis (RDA).
TransformIntoBinary.m: Transforms a species data matrix based on abundnace into a presence/absence (binary) species data matrix.
VariationPartitionRDA.m: variation partitioning on a data table Y based on two data matrices X and W based on redundancy analysis (RDA). Tests of significance on fractions are performed by permutation.
ExampleSimulationFile.m: one of the files used to run a complete set of simulations. The files used in this example (Envir025.txt and Species025.txt) relates to Scenario D in Table 1 of the main paper.
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