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nestedrank

Calculates the rank of a species in a matrix sorted for maximum nestedness


Description

Ranks species according to their generality, which is measured as the position in the nestedness matrix. A generalist will interact with more species and thus have a rank closer to 1, while specialists (and rare species) will have ranks with higher values.

Usage

nestedrank(web, method = "NODF", weighted=TRUE, normalise=TRUE, return.matrix=FALSE)

Arguments

web

A matrix with elements of a set (e.g., plants) as rows, elements of a second set (e.g., pollinators) as columns and number of interactions as entries.

method

One or more of the following: NODF, nodf, binmatnest, wine, sort. See details for details on each method.

weighted

For NODF and wine only: should the number of interactions per link be used as weights? See help of nestednodf in vegan for details.

normalise

Logical; defaulting to TRUE. Divides the rank-1 by the number of species -1, thereby ranging it between 0 (most generalist) and 1 (most specialised).

return.matrix

Logical, defaulting to FALSE. Should the matrix resulting from the nestedness-sorting be returned as well?

Details

The idea is to re-arrange the network matrix according to its nestedness, so that the most “generalist” species with most links will be in the first row/column and decreasing from there. The nestedness matrix can be computed in different ways. There are four different methods currently available:

NODF (or nodf)

will use vegan's nestednodf-function to arrange the matrix. With weighted=TRUE, which is the default, it will use the actual number of interactions, rather than the number of links

binmatnest

will use the vegan's nestedtemp-function to arrange the matrix. This is only using binary information, so weighting has no effect.

wine

will use the wine-function to arrange the matrix. When weighted=FALSE, wine will be applied to a binary matrix.

sort

will simply sort the matrix by marginal totals (i.e. by number of interactions per species when weighted=TRUE or by number links (=degree) when weighted=FALSE. In this case the rank simply represents the abundance of the species in this network.

Value

A list of nestedness ranks vectors for the lower and higher trophic level (smallest value for the most generalist). If return.matrix=TRUE, a third list entry will contain the nested matrix.

Note

Since nestedness is itself not a straight-forward measure of something ecologically meaningful, also these ranks may or may not be. At least there is a high chance that they represent merely abundance of each species. See example for an idea on how to check for the effect of abundance as such.

Author(s)

References

Alarcon, R., Waser, N.M. and Ollerton, J. 2008. Year-to-year variation in the topology of a plant-pollinator interaction network. Oikos 117, 1796–1807

See Also

Examples

## Not run: 
ranks <- sapply(c("nodf", "binmatnest", "wine", "sort"), function(x) 
  nestedrank(Safariland, method=x)[[2]])
cor(ranks) # high correlation between sort and other indicate that only abundance matters

## End(Not run)

bipartite

Visualising Bipartite Networks and Calculating Some (Ecological) Indices

v2.16
GPL
Authors
Carsten F. Dormann, Jochen Fruend and Bernd Gruber, with additional code from Stephen Beckett, Mariano Devoto, Gabriel Felix, Jose Iriondo, Tove Opsahl, Rafael Pinheiro, Rouven Strauss and Diego Vazquez, also based on C-code developed by Nils Bluethgen, Aaron Clauset/Rouven Strauss and Miguel Rodriguez-Girones
Initial release
2021-02-08

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