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

Description Details Author(s) Examples

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, ordination methods and tools for the analysis of dissimilarities. 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 GitHub (https://github.com/vegandevs/vegan/). GitHub provides up-to-date information and forums for bug reports.

Most important changes in vegan documents can be read with news(package="vegan") and vignettes can be browsed with browseVignettes("vegan"). The vignettes include a vegan FAQ, discussion on design decisions, short introduction to ordination and discussion on diversity methods. 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").

Author(s)

The vegan development team is Jari Oksanen, F. Guillaume Blanchet, Roeland Kindt, Pierre Legendre, Peter R. Minchin, R. B. O'Hara, Gavin L. Simpson, Peter Solymos, M. Henry H. Stevens, Helene Wagner. Many other people have contributed to individual functions: see credits in function help pages.

Examples

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### 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)

Example output

Loading required package: permute
Loading required package: lattice
This is vegan 2.4-3
Square root transformation
Wisconsin double standardization
Run 0 stress 0.1843196 
Run 1 stress 0.18458 
... Procrustes: rmse 0.04943018  max resid 0.1579102 
Run 2 stress 0.234577 
Run 3 stress 0.1962451 
Run 4 stress 0.21377 
Run 5 stress 0.20931 
Run 6 stress 0.1852397 
Run 7 stress 0.2109611 
Run 8 stress 0.2143689 
Run 9 stress 0.2499291 
Run 10 stress 0.2096851 
Run 11 stress 0.1985584 
Run 12 stress 0.2112259 
Run 13 stress 0.2061122 
Run 14 stress 0.2253398 
Run 15 stress 0.2075297 
Run 16 stress 0.2101147 
Run 17 stress 0.2459537 
Run 18 stress 0.2114468 
Run 19 stress 0.1852397 
Run 20 stress 0.2363955 
*** No convergence -- monoMDS stopping criteria:
    20: stress ratio > sratmax

***VECTORS

            NMDS1    NMDS2     r2 Pr(>r)    
N        -0.05038 -0.99873 0.2080  0.085 .  
P         0.68719  0.72647 0.1755  0.115    
K         0.82745  0.56155 0.1657  0.159    
Ca        0.75024  0.66116 0.2809  0.029 *  
Mg        0.69691  0.71716 0.3492  0.016 *  
S         0.27645  0.96103 0.1774  0.118    
Al       -0.83757  0.54633 0.5155  0.001 ***
Fe       -0.86169  0.50743 0.3999  0.004 ** 
Mn        0.80219 -0.59707 0.5323  0.003 ** 
Zn        0.66537  0.74651 0.1779  0.124    
Mo       -0.84867  0.52892 0.0517  0.582    
Baresoil  0.87189 -0.48971 0.2494  0.052 .  
Humdepth  0.92623 -0.37696 0.5590  0.001 ***
pH       -0.79900  0.60133 0.2625  0.038 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Permutation: free
Number of permutations: 999


Call: rda(formula = dune ~ 1, data = dune.env)

              Inertia Rank
Total           84.12     
Unconstrained   84.12   19
Inertia is variance 

Eigenvalues for unconstrained axes:
   PC1    PC2    PC3    PC4    PC5    PC6    PC7    PC8 
24.795 18.147  7.629  7.153  5.695  4.333  3.199  2.782 
(Showed only 8 of all 19 unconstrained eigenvalues)

Call: rda(formula = dune ~ A1 + Moisture + Management + Use + Manure,
data = dune.env)

              Inertia Proportion Rank
Total         84.1237     1.0000     
Constrained   63.2062     0.7513   12
Unconstrained 20.9175     0.2487    7
Inertia is variance 
Some constraints were aliased because they were collinear (redundant)

Eigenvalues for constrained axes:
  RDA1   RDA2   RDA3   RDA4   RDA5   RDA6   RDA7   RDA8   RDA9  RDA10  RDA11 
22.396 16.208  7.039  4.038  3.760  2.609  2.167  1.803  1.404  0.917  0.582 
 RDA12 
 0.284 

Eigenvalues for unconstrained axes:
  PC1   PC2   PC3   PC4   PC5   PC6   PC7 
6.627 4.309 3.549 2.546 2.340 0.934 0.612 


Start: dune ~ 1 

             Df    AIC      F Pr(>F)   
+ Management  3 87.082 2.8400  0.005 **
+ Moisture    3 87.707 2.5883  0.005 **
+ Manure      4 89.232 1.9539  0.005 **
+ A1          1 89.591 1.9217  0.060 . 
+ Use         2 91.032 1.1741  0.305   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Step: dune ~ Management 

             Df   AIC    F Pr(>F)   
- Management  3 89.62 2.84  0.005 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

           Df    AIC      F Pr(>F)   
+ Moisture  3 85.567 1.9764  0.005 **
+ Manure    3 87.517 1.3902  0.100 . 
+ A1        1 87.424 1.2965  0.180   
+ Use       2 88.284 1.0510  0.335   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Step: dune ~ Management + Moisture 

             Df    AIC      F Pr(>F)   
- Moisture    3 87.082 1.9764  0.010 **
- Management  3 87.707 2.1769  0.005 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

         Df    AIC      F Pr(>F)
+ Manure  3 85.762 1.1225  0.320
+ A1      1 86.220 0.8359  0.625
+ Use     2 86.842 0.8027  0.760

Call: rda(formula = dune ~ Management + Moisture, data = dune.env)

              Inertia Proportion Rank
Total         84.1237     1.0000     
Constrained   46.4249     0.5519    6
Unconstrained 37.6988     0.4481   13
Inertia is variance 

Eigenvalues for constrained axes:
  RDA1   RDA2   RDA3   RDA4   RDA5   RDA6 
21.588 14.075  4.123  3.163  2.369  1.107 

Eigenvalues for unconstrained axes:
  PC1   PC2   PC3   PC4   PC5   PC6   PC7   PC8   PC9  PC10  PC11  PC12  PC13 
8.241 7.138 5.355 4.409 3.143 2.770 1.878 1.741 0.952 0.909 0.627 0.311 0.227 

Permutation test for rda under reduced model
Permutation: free
Number of permutations: 999

Model: rda(formula = dune ~ Management + Moisture, data = dune.env)
         Df Variance      F Pr(>F)    
Model     6   46.425 2.6682  0.001 ***
Residual 13   37.699                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Permutation test for rda under reduced model
Marginal effects of terms
Permutation: free
Number of permutations: 999

Model: rda(formula = dune ~ Management + Moisture, data = dune.env)
           Df Variance      F Pr(>F)   
Management  3   18.938 2.1769  0.007 **
Moisture    3   17.194 1.9764  0.011 * 
Residual   13   37.699                 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Family: gaussian 
Link function: identity 

Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)

Estimated degrees of freedom:
1.28  total = 2.28 

REML score: 3.00623     

Call:
adonis(formula = dune ~ ., data = dune.env) 

Permutation: free
Number of permutations: 999

Terms added sequentially (first to last)

           Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)   
A1          1    0.7230 0.72295  5.2038 0.16817  0.002 **
Moisture    3    1.1871 0.39569  2.8482 0.27613  0.006 **
Management  3    0.9036 0.30121  2.1681 0.21019  0.027 * 
Use         2    0.0921 0.04606  0.3315 0.02143  0.980   
Manure      3    0.4208 0.14026  1.0096 0.09787  0.482   
Residuals   7    0.9725 0.13893         0.22621          
Total      19    4.2990                 1.00000          
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Call:
adonis(formula = dune ~ Management + Moisture, data = dune.env) 

Permutation: free
Number of permutations: 999

Terms added sequentially (first to last)

           Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
Management  3    1.4686 0.48953  3.7907 0.34161  0.001 ***
Moisture    3    1.1516 0.38387  2.9726 0.26788  0.001 ***
Residuals  13    1.6788 0.12914         0.39051           
Total      19    4.2990                 1.00000           
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

vegan documentation built on May 31, 2017, 4:08 a.m.