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.
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: , , , , . 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?
Value
A list of nestedness ranks vectors for the lower and higher trophic level (smallest value for the most generalist). If , a third list entry will contain the nested matrix.
encoding
UTF-8
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:
[object Object],[object Object],[object Object],[object Object]
References
Alarcon, R., Waser, N.M. and Ollerton, J. 2008. Year-to-year variation in the topology of a plant-pollinator interaction network. Oikos117, 1796--1807
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