simulSADcomm: Simulate community matrix with constant SAD

In ordiconsensus: Consensus of canonical ordinations through the canonical redundancy analysis

Description

This function simulates community matrices with the same species abundance distribution following patterns defined by a set of explanatory variables. This function was used to simulate community matrices in Blanchet et al. (In press)

Usage

simulSADcomm(sp.abund, expl.var, expl.rand.sel = TRUE, nexpl.comb = 2, 
             binary = FALSE, fix.expl = NULL, nsite = 50, weight = NULL, 
             range.weight = c(0, 2), sd.expl = FALSE, norm = c(0, 1))

Arguments

sp.abund	A vector defining the number of species in a bin. See Details for more information.
expl.var	A matrix of explanatory variables to use to construct the species.
expl.rand.sel	Logical. Whether an explanatory variable should be randomly selected to construct a species (TRUE) or a fixed combination should be given (FALSE). (Default is TRUE)
nexpl.comb	Numeric. The number of explanatory variables that will be combined together to construct a species. Default is 2.
binary	Logical. Whether the site-by-species matrix presents abundances (FALSE) or presence/absence (TRUE). Default is FALSE.
fix.expl	A matrix that defines which combination of explanatory variables should be used to construct species. This argument is only active when expl.rand.sel=FALSE. See Details for more information.
nsite	Numeric. Number of sites (rows) in the resulting community matrix. See Details.
weight	A vector of regression coefficient used to give weight to each species. If NULL, weights are selected by randomly samping of a uniform distribution with a range defined by range.weight. Default is NULL.
range.weight	A vector of length 2 giving the minimum and maximum of a uniform distribution from which the weight will be sampled. Default is 0 and 2.
sd.expl	Logical. This argument is only active when expl.rand.sel is FALSE (That is when a fixed combination of explanatory variable is used to construct a community matrix). This could be either the standard deviation of the Normal error added when constructing a species is a multiplier of the standard deviation of the deterministic portion of the newly created explanatory variable (TRUE) or the pure standard deviation (FALSE). Default is FALSE.
norm	Vector of length 2 giving the mean and a multiplier of the standard deviation of the deterministic portion of a newly created explanatory variable. Default is mean = 0 and multiplier of the standard deviation of the new deterministic explanatory variable = 1.

Details

The argument sp.abund defines the species-abundance distribution structure of the data following the binning proposed by Gray et al. (2006). For example, if the vector is (40,20,30), it means that there will be 40 species with 1 individual, 20 with 2 or 3 individuals, and 30 with 4 to 7 individuals.

The individuals are assigned to the sites according to the set of exlanatory variables given in expl.var. It is possible that a site occur with 0 individuals. They will be included in the community matrix and should be dealt with a posteriori.

If expl.rand.sel is TRUE, the explanatory variables are randomly sampled (without replacement) when combining (adding) explanatory variables together. The number of explanatory variables must be a multiple of nexpl.comb.

Error is included to a species by multiplying a weight to the explanatory variable used to construct the species and by adding a normally distributed error term to the same explanatory variable. An error term with a standard deviation equal to the standard deviation of the explanatory variable allows for the explanatory variable to explain roughly 50

fix.expl is a matrix that has as many rows as there are species and as many columns as nexpl.comb (number of explanatory variables to combine). The numbers in fix.expl are integers that refers to the columns of expl.var. When fix.expl is TRUE, nexpl.comb becomes meaningless.

If a presence-absence matrix is constructed (binary=TRUE), sp.abund should be constructed in such a way that no bin should include species with an abundance larger than the number of sites, otherwise an error message will be sent. Within, this constraint, if the maximum of the last bin (the one with the largest abundance) is larger than the number of site, it will be automatically changed to the number of sites-1.

This function was designed to do much more than the simulations generated in the work of Blanchet et al. (in press). It is meant to be used for future simulation studies.

Value

site.sp : The site (rows) by species (column) community matrix generated. sel.expl : A vector presenting the order explanatory variables used to construct each species. The order follows the order of the species.

Author(s)

F. Guillaume Blanchet

References

Gray, J.S., A. Bjorgeaeter, and K.I. Ugland. 2006. On plotting species abundance distributions, Journal of Animal Ecology 75:752–756.

Blanchet, F.G., P. Legendre, J.A.C. Bergeron, F. He. In press. Consensus RDA across dissimilarity coefficients for canonical ordination of community composition data, Ecological Monographs.

Examples

SAD<-c(1,2,4,6,4,2,1,0,0,0)
expl<-matrix(rnorm(400),ncol=8)
simulSADcomm(SAD,expl)

ordiconsensus documentation built on May 2, 2019, 4:38 p.m.