bipartite (version 2.16)

strength: Computes species strength according to either of two definitions

Description

Computes species strength of the higher level species as a measure of how important a species is in the network

Usage

strength(web, type="Bascompte")

Arguments

web

A matrix with lower trophic level species as rows, higher trophic level species as columns and number of interactions as entries.

type

Which definition of species strength should be used, Bascompte (default) or Barrat? See Details for definitions.

Value

A vector of species strengths for the higher level. Employ this function on the transpose of the web to compute the strengths of the lower level (see example).

Details

There are two definitions of species strength, that of Bascompte et al. (2006) as the sum of dependencies of a species, and that of Barrat et al. (2004) as the weighted sum of links. As a consequence, Bascompte et al.'s strength sums to the number of species in the other group, while Barrat et al.'s strength is simply the number of interactions, a trivial measure of a species importance.

In contrast to the claim of Gilarranz et al. (2012, p. 1155), this definition of strength gives no information of the centrality of a species within a network structure (and neither does Bascompte et al.'s).

References

Barrat, A., Barth<U+00E9>lemy, M., Pastor-Satorras, R. & Vespignani, A. (2004) The architecture of complex weighted networks. Proceedings of the National Academy of Sciences of the USA 101, 3747-<U+2013>3752

Bascompte, J., Jordano, P. & Olesen, J.M. (2006) Asymmetric coevolutionary networks facilitate biodiversity maintenance. Science 312, 431-<U+2013>433

Gilarranz, L.J., Pastor, J.M. & Galeano, J. (2012) The architecture of weighted mutualistic networks. Oikos 121, 1154-<U+2013>1162

See Also

specieslevel which could (but doesn't yet) call strength (instead it uses the default always)

Examples

# NOT RUN {
data(Safariland)
s1 <- strength(Safariland, type="Barrat")
s2 <- strength(Safariland, type="Bascompte")
plot(s1, s2, log="x")
cor.test(s1, s2, type="ken")
# for lower level:
strength(t(Safariland))

# }