Description

Distance-based ordination (dbrda, capscale, metaMDS) have no information on species, but some methods may add species scores if community data were available. However, the species scores may be missing (and they always are in dbrda), or they may not have a close relation to used dissimilarity index. This function will add the species scores or replace the existing species scores in distance-based methods.

Usage

Arguments

Details

Distances have no information on species (columns, variables), and hence distance-based ordination has no information on species scores. However, the species scores can be added as supplementary information after the analysis to help the interpretation of results. Some ordination methods (capscale, metaMDS) can supplement the species scores during the analysis if community data was available in the analysis.

In capscale the species scores are found by projecting the community data to site ordination (linear combination scores), and the scores are accurate if the analysis used Euclidean distances. If the dissimilarity index can be expressed as Euclidean distances of transformed data (for instance, Chord and Hellinger Distances), the species scores based on transformed data will be accurate, but the function still finds the dissimilarities with untransformed data. Usually community dissimilarities differ in two significant ways from Euclidean distances: They are bound to maximum 1, and they use absolute differences instead of squared differences. In such cases, it may be better to use species scores that are transformed so that their Euclidean distances have a good linear relation to used dissimilarities. It is often useful to standardize data so that each row has unit total, and perform squareroot transformation to damp down the effect of squared differences (see Examples).

Function dbrda never finds the species scores, but it is mathematically similar to capscale, and similar rules should be followed when supplementing the species scores.

Function metaMDS uses weighted averages (wascores) to find the species scores. These have a better relationship with most dissimilarities than the projection scores used in metric ordination, but similar transformation of the community data should be used both in dissimilarities and in species scores.

Value

Replacement function adds the species scores or replaces the old scores in the ordination object.

Author(s)

Jari Oksanen

See Also

Function envfit finds similar scores, but based on correlations. The species scores for non-metric ordination use wascores which can also used directly on any ordination result.

Examples