null.t.test {bipartite}

Compares observed pattern to random webs.

Description

A little null-model function to check, if the observed values actually are much different to what one would expect under random numbers given the observed row and column totals (i.e.~information in the structure of the web, not only in its species' abundances). Random matrices are based on the function r2dtable. The test itself is a t-test (with all its assumptions).

Usage

null.t.test(web, N = 30, ...)

Arguments

web

A matrix representing the interactions observed between higher trophic level species (columns) and lower trophic level species (rows).

N

Number of null models to be produced; see ‘Note’ below!

...

Optional parameters to be passed on to the functions networklevel and t.test.

Details

This is only a very rough null-model test. There are various reasons why one may consider r2dtable as an incorrect way to construct null models (e.g.~because it yields very different connectance values compared to the original). It is merely used here to indicate into which direction a proper development of null models may start off. Also, if the distribution of null models is very skewed, a t-test is obviously not the test of choice.

Finally, not all indices will be reasonably testable (e.g.~number of species is fixed), or are returned by the function networklevel in a form that null.t.test can make use of (e.g.~degree distribution fits).

Value

Returns a table with one row per index, and columns giving

obs

observed value

null mean

mean null model value

lower CI

lower 95% confidence interval (or whatever level is specified in the function's call)

upper CI

upper 95% confidence interval (or whatever level is specified in the function's call)

t

t-statistic

P

P-value of t statistic

Note

This function is rather slow. Using large replications in combination with iterative indices (degree distribution, compartment diversity, extinction slope, H2) may lead to rather long runtimes!

Author(s)

Carsten F. Dormann [e-mail]

Examples

data(mosquin1967)
null.t.test(mosquin1967, index=c("generality", "vulnerability",
    "cluster coefficient", "H2", "ISA", "SA"), nrep=2, N=10)