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vegan (version 2.0-10)

simper: Similarity Percentages

Description

Discriminating species between two groups using Bray-Curtis dissimilarities

Usage

simper(comm, group,  ...)
## S3 method for class 'simper':
summary(object, ordered = TRUE, 
     digits = max(3, getOption("digits") - 3), ...)

Arguments

comm
Community data matrix.
group
Factor describing the group structure. Must have at least 2 levels.
object
an object returned by simper.
ordered
Logical; Should the species be ordered by their average contribution?
digits
Number of digits in output.
...
Parameters passed to other functions.

Value

  • A list of class "simper" with following items:
  • speciesThe species names.
  • averageAverage contribution to overall dissimilarity.
  • overallThe overall between-group dissimilarity.
  • sdStandard deviation of contribution.
  • ratioAverage to sd ratio.
  • ava, avbAverage abundances per group.
  • ordAn index vector to order vectors by their contribution or order cusum back to the original data order.
  • cusumOrdered cumulative contribution.

encoding

UTF-8

Details

Similarity percentage, simper (Clarke 1993) is based on the decomposition of Bray-Curtis dissimilarity index (see vegdist, designdist). The contribution of individual species $i$ to the overall Bray-Curtis dissimilarity $d_{jk}$ is given by

$$d_{ijk} = \frac{|x_{ij}-x_{ik}|}{\sum_{i=1}^S (x_{ij}+x_{ik})}$$ where $x$ is the abundance of species $i$ in sampling units $j$ and $k$. The overall index is the sum of the individual contributions over all $S$ species $d_{jk}=\sum_{i=1}^S d_{ijk}$. The simper functions performs pairwise comparisons of groups of sampling units and finds the average contributions of each species to the average overall Bray-Curtis dissimilarity.

The function displays most important species for each pair of groups. These species contribute at least to 70 % of the differences between groups. The function returns much more extensive results which can be accessed directly from the result object (see section Value). Function summary transforms the result to a list of data frames. With argument ordered = TRUE the data frames also include the cumulative contributions and are ordered by species contribution.

The results of simper can be very difficult to interpret. The method very badly confounds the mean between group differences and within group variation, and seems to single out variable species instead of distinctive species (Warton et al. 2012). Even if you make groups that are copies of each other, the method will single out species with high contribution, but these are not contributions to non-existing between-group differences but to within-group variation in species abundance.

References

Clarke, K.R. 1993. Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology, 18, 117–143.

Warton, D.I., Wright, T.W., Wang, Y. 2012. Distance-based multivariate analyses confound location and dispersion effects. Methods in Ecology and Evolution, 3, 89--101.

Examples

Run this code
data(dune)
data(dune.env)
(sim <- with(dune.env, simper(dune, Management)))
summary(sim)

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