Theory, Examples & Exercises
This is an old revision of the document!
Zelený D. (unpubl.), script is based on the simulation model written by Fridley et al. (2007) (see Appendix S2 of their paper), which itself was based on the work of Minchin (1987).
These simulated community datasets represent the model of community, which is fully based on the ecological niche theory. Unimodal species response curves are randomly distributed along one (or two, respectively) virtual ecological gradients, reflecting the probability of species occurrence in given part of the gradient (species response curve is based on Beta function). Each species is defined by its ecological optimum along the gradient, niche width, maximum probability of occurrence and few other parameters. In the next step, random positions along gradient are generated, and within each position (“sample”) are collected individual species in the following way: first, random number is generated, corresponding to the number of individuals in a given sample; than, each individual is randomly assigned to a species and probability of the assignment to given species is weighted by probability of this species occurrence in particular part of the gradient. One species could be hence assigned to more individuals per sample, if its probability of occurrence in given part of the gradient is high. In case of two virtual gradients, the probability of occurrence for particular species is given by multiplying the probabilities of given species along each of the gradient. For details, see the scripts below.
Note: number of species (e.g. 300) is a parameter set up for simulated models - number of species in the resulting community matrix doesn't have to fit to number of simulated species, as some of the species with low abundance were not “sampled”.
-spe in the name represent presence-absence matrix of species data, files with
-env contain position of simulated sample along virtual gradient (analogy to measured environmental variable), files with
-specvalues contain information about position of species optima along the gradient and niche width (both in arbitrary gradient units).
simul1.spe <- read.delim ('http://www.davidzeleny.net/anadat-r/data-download/simul1-spe.txt', row.names = 1) simul1.env <- read.delim ('http://www.davidzeleny.net/anadat-r/data-download/simul1-env.txt', row.names = 1) simul1.specvalues <- read.delim ('http://www.davidzeleny.net/anadat-r/data-download/simul1-specvalues.txt', row.names = 1) simul2.spe <- read.delim ('http://www.davidzeleny.net/anadat-r/data-download/simul2-spe.txt', row.names = 1) simul2.env <- read.delim ('http://www.davidzeleny.net/anadat-r/data-download/simul2-env.txt', row.names = 1) simul2.specvalues <- read.delim ('http://www.davidzeleny.net/anadat-r/data-download/simul2-specvalues.txt', row.names = 1) simul3.spe <- read.delim ('http://www.davidzeleny.net/anadat-r/data-download/simul3-spe.txt', row.names = 1) simul3.env <- read.delim ('http://www.davidzeleny.net/anadat-r/data-download/simul3-env.txt', row.names = 1) simul3.specvalues <- read.delim ('http://www.davidzeleny.net/anadat-r/data-download/simul3-specvalues.txt', row.names = 1) simul.short.spe <- read.delim ('http://www.davidzeleny.net/anadat-r/data-download/simul.short-spe.txt', row.names = 1) simul.short.env <- read.delim ('http://www.davidzeleny.net/anadat-r/data-download/simul.short-env.txt', row.names = 1) simul.long.spe <- read.delim ('http://www.davidzeleny.net/anadat-r/data-download/simul.long-spe.txt', row.names = 1) simul.long.env <- read.delim ('http://www.davidzeleny.net/anadat-r/data-download/simul.long-env.txt', row.names = 1)
CommEcolwritten by Adriano Sanches Melo should be direct implementation of Minchin's software COMPAS (Minchin 1987). The principles are similar (actually Fridley et al. 2007 paper cites the Minchin's paper using COMPAS), but
compasallows generation of community matrix in more than two dimensions, and adding quantitative and qualitative noise.
coenoclinerdeveloped by Gavin Simpson - apart to Minchin's model, it can simulate bunch of other types of community data along coenocline.