Shuffle web entries
Shuffles (= relocates) entries in a web matrix whilst maintaining the dimensionality.
shuffle.web(web, N, legacy=TRUE)
An interaction matrix.
Number of desired shuffled matrices.
Logical; use the old or new algorithm? Defaults to TRUE, i.e. the old algorithm. The new algorithm was written by Paul Rabie and is about 3 times faster (due to avoiding a loop). For consistency reasons, the old, slow algorithm remains the default.
This function is designed to behave similar to
r2dtable, i.e. it returns a list of randomised matrices.
In contrast to
r2dtable is does not keep marginal sums constant!
This function is thought of as a null model for the analysis of bipartite webs. It keeps two web properties
constant: The number of interactions and the number of links (and hence connectance). A comparison of
r2dtable-based webs allows to elucidate the effect of marginal sums.
A list of N randomised matrices with the same dimensions as the initial web.
shuffle.web is not an extremely intelligent nullmodel. You may want to think of a better one for your specific application!
Carsten F. Dormann <firstname.lastname@example.org>
This null model can be thought of as a quantitative version of Fortuna & Bascompte (2006) “null model 1”:
Fortuna, M. A., and J. Bascompte. 2006. Habitat loss and the structure of plant-animal mutualistic networks. Ecology Letters 9: 281-286.
For a very nice and thorough overview of null models in general see:
Gotelli, N. J., and G. R. Graves. 1996. Null Models in Ecology. Smithsonian Institution Press, Washington D.C.
For null models and their application to webs/networks see, e.g.:
Vázquez, D. P., and M. A. Aizen. 2003. Null model analyses of specialization in plant-pollinator interactions. Ecology 84: 2493-2501.
Vázquez, D. P., C. J. Melian, N. M. Williams, N. Blüthgen, B. R. Krasnov, and R. Poulin. 2007. Species abundance and asymmetric interaction strength in ecological networks. Oikos 116: 1120-1127.
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data(Safariland) shuffle.web(Safariland, N=2)
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