Description Usage Arguments Details Value Author(s) References Examples

Discriminating species between two groups using Bray-Curtis dissimilarities

1 2 3 4 5 |

`comm` |
Community data matrix. |

`group` |
Factor describing the group structure. Must have at least 2 levels. |

`permutations` |
a list of control values for the permutations
as returned by the function |

`trace` |
Trace permutations. |

`object` |
an object returned by |

`ordered` |
Logical; Should the species be ordered by their average contribution? |

`digits` |
Number of digits in output. |

`parallel` |
Number of parallel processes or a predefined socket
cluster. With |

`...` |
Parameters passed to other functions. In |

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] = abs(x[ij]-x[ik])/sum(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.

A list of class `"simper"`

with following items:

`species` |
The species names. |

`average` |
Average contribution to overall dissimilarity. |

`overall` |
The overall between-group dissimilarity. |

`sd` |
Standard deviation of contribution. |

`ratio` |
Average to sd ratio. |

`ava, avb` |
Average abundances per group. |

`ord` |
An index vector to order vectors by their contribution or
order |

`cusum` |
Ordered cumulative contribution. |

`p` |
Permutation |

Eduard Szöcs eduardszoecs@gmail.com

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.

1 2 3 4 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.