nested {bipartite} | R Documentation |
Wrapper function calling one, several or all currently implemented nestedness measures
nested(web, method = "binmatnest2", ..., rescale = FALSE)
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: discrepancy, discrepancy2, binmatnest, binmatnest2, NODF, NODF2, C.score, checker, wine, ALL. See details for details on each method |
... |
Arguments passed on to other nestedness functions. Options need to be specified (i.e. no positional parsing). |
rescale |
Should all measures be rescaled so that higher values mean higher nestedness? Defaults to FALSE, i.e. the standard interpretation of each measure is maintained. |
There are five different measures currently available:
nestedness
(0 = cold = highly nested; 100 = hot = not nested at all). It uses the original program of Miguel Rodríguez-Gironés, only called from R; binmatnest2, in contrast, is the implementation in nestedtemp
of the same algorithm by Jari Oksanen. Because binmatnest sometimes (and to us unexplicably) invert the matrix, we prefer the binmatnest2 option.discrepancy
calls the function with the same name, discrepancy2 calls nesteddisc
, which handles ties differently. Most of the time, these two should deliver very, very similar results. Higher values indicate lower nestedness.nestednodf
in vegan. (Yes, I initially programmed NODF myself, only to find that it was there already. Luckily, there was a perfect agreement between my (depricated) version and nestednodf.)C.score
calculates the number of checkerboard pattern in the matrix. As default, it normalises this value between min and max, so that values of 0 indicate no checkerboards (i.e. nesting), while a value of 1 indicates a perfect checkerboard. checker is the non-normalised version, based on nestedchecker
.wine
for details.A vector with values for each of the selected nestedness measures.
The idea behind this function is to encourage the comparison of different nestedness measures. That does not mean, we necessarily see much ecological sense in them (see, e.g., the paper by Blüthgen et al. 2008).
Carsten F. Dormann carsten.dormann@ufz.de
Almeida-Neto, M., Gumaraes, P., Gumaraes, P.R., Loyola, R.D. and Ulrich, W. 2008. A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117, 1227–1239.
Blüthgen, N., J. Fründ, D. P. Vázquez, and F. Menzel. 2008. What do interaction network metrics tell us about specialisation and biological traits? Ecology 89, 3387–3399.
Brualdi, R.A. and Sanderson, J.G. 1999. Nested species subsets, gaps, and discrepancy. Oecologia 119, 256–264.
Galeano, J., Pastor, J.M., Iriondo and J.M. 2008. Weighted-Interaction Nestedness Estimator (WINE): A new estimator to calculate over frequency matrices. arXiv 0808.3397v2 [physics.bio-ph]
Rodriguez-Girones, M.A. and Santamaria, L. 2006. A new algorithm to calculate the nestedness temperature of presence-absence matrices. J. Biogeogr. 33, 924–935.
Stone, L. and Roberts, A. 1990. The checkerboard score and species distributions. Oecologia 85, 74–79.
C.score
, wine
, nestedness
, discrepancy
; and, within vegan: nestedtemp
, nestedchecker
, nesteddisc
, nestednodf
data(Safariland) nested(Safariland, "ALL") nested(Safariland, "ALL", rescale=TRUE) # illustration that non-normalised C.score and checker are the same: nested(Safariland, c("C.score", "checker"), normalise=FALSE)