vegan (version 2.0-10)

vegan-package: Community Ecology Package: Ordination, Diversity and Dissimilarities

Description

The vegan package provides tools for descriptive community ecology. It has most basic functions of diversity analysis, community ordination and dissimilarity analysis. Most of its multivariate tools can be used for other data types as well.

Details

The functions in the vegan package contain tools for diversity analysis (see vignette vegandocs("diversity")), ordination and analysis of dissimilarities (see vignette vegandocs("intro")). Together with the labdsv package, the vegan package provides most standard tools of descriptive community analysis. Package ade4 provides an alternative comprehensive package, and several other packages complement vegan and provide tools for deeper analysis in specific fields. Package BiodiversityR provides a GUI for a large subset of vegan functionality.

The vegan package is developed at R-Forge (http://vegan.r-forge.r-project.org). The R-Forge provides up-to-date information and mailing lists for help queries and bug reports. Bug reports can also be emailed to the function authors or to the package maintainers.

The vegan documents can be read with vegandocs function. In addition to vignettes of basic usage, you can read NEWS on the new features and bug fixes in the release version (vegandocs("NEWS")), and more technical and fine grained ChangeLog (vegandocs("Change")). Several frequently asked questions really are answered in the vegan FAQ (vegandocs("FAQ")). The discussion on design decisions can be read with vegandocs("decision"). A tutorial of the package at http://cc.oulu.fi/~jarioksa/opetus/metodi/vegantutor.pdf provides a more thorough introduction to the package.

To see the preferable citation of the package, type citation("vegan").

Examples

### Example 1: Unconstrained ordination
## NMDS
data(varespec)
data(varechem)
ord <- metaMDS(varespec)
plot(ord, type = "t")
## Fit environmental variables
ef <- envfit(ord, varechem)
ef
plot(ef, p.max = 0.05)
### Example 2: Constrained ordination (RDA)
## The example uses formula interface to define the model
data(dune)
data(dune.env)
## No constraints: PCA
mod0 <- rda(dune ~ 1, dune.env)
mod0
plot(mod0)
## All environmental variables: Full model
mod1 <- rda(dune ~ ., dune.env)
mod1
plot(mod1)
## Automatic selection of variables by permutation P-values
mod <- ordistep(mod0, scope=formula(mod1))
mod
plot(mod)
## Permutation test for all variables
anova(mod)
## Permutation test of "type III" effects, or significance when a term
## is added to the model after all other terms
anova(mod, by = "margin")
## Plot only sample plots, use different symbols and draw SD ellipses 
## for Managemenet classes
plot(mod, display = "sites", type = "n")
with(dune.env, points(mod, disp = "si", pch = as.numeric(Management)))
with(dune.env, legend("topleft", levels(Management), pch = 1:4,
  title = "Management"))
with(dune.env, ordiellipse(mod, Management, label = TRUE))
## add fitted surface of diversity to the model
ordisurf(mod, diversity(dune), add = TRUE)
### Example 3: analysis of dissimilarites a.k.a. non-parametric
### permutational anova
adonis(dune ~ ., dune.env)
adonis(dune ~ Management + Moisture, dune.env)