Acts as a wrapper for the
specieslevel functions, allowing a wide range
of measures to be calculated (Dormann et al., 2008, 2009; Dorman
2011). These are calculated both for the observed network and across the
iterations of the null model, allowing for simple tests of whether the
observed values differ from those expected by chance.
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An object of class 'nullnet' from
An optional value specifying the threshold used for testing for 'significant' deviations from the null model. Defaults to 0.95
String specifying which function to call from the
Vector listing the bipartite network statistics to calculate.
All indices are currently supported, with the exception of the dependence
A logical value specifying whether the progress count
should be shown. Defaults to
Other arguments that may be supplied to
Allows most of the network metrics in the
to be calculated for an observed bipartite network and compared to the
distribution of those network metrics across the iterations of the null
model. This indicates whether the observed network differs from the
structure of the network that could be expected if consumers simply used
resources in proportion to their relative abundance.
The user sets the significance level (default = 95% confidence limits), and the metrics selected are classified into those that are higher, lower or consistent with the null model at that significance level. Significance is determined by comparing the observed value of the statistic to the 1-alpha/2 percentiles from the frequency distribution, with 'significant' values falling outside the confidence interval (Manly 2006).
Returns one or more data frames according to the level at which the
statistics are calculated (
specieslevel, grouplevel or
index.type = "networklevel" or
index.type = "grouplevel" a single data frame is returned, listing
the chosen network statistics and with the following column headings:
Value of the statistic for the observed network
Mean value of the statistic across the iterations of the null model
Lower confidence limit for the metric
Upper confidence limit for the network metric
Whether the value of the statistic with the observed network is significantly higher than expected under the null model, lower or consistent with the null model (ns)
The standardised effect size for the difference between the observed network and the null model (see Gotelli & McCabe 2002 for details)
index.type = "specieslevel", a list comprising two data frames for
each statistic, representing the
lower levels in
the network. Each data frame has the same format as for
networklevel except that the rows are individual nodes (species)
in the network. See examples for how to call the individual data frames.
Dormann, C.F., Gruber B. & Frund, J. (2008). Introducing the bipartite package: analysing ecological networks. R news, 8, 8–11.
Dormann, C.F., Frund, J., Bluthgen, N. & Gruber, B. (2009). Indices, graphs and null models: analyzing bipartite ecological networks. Open Ecology Journal 2, 7–24.
Dormann, C.F. (2011) How to be a specialist? Quantifying specialisation in pollination networks. Network Biology, 1, 1-20.
Gotelli, N.J. & McCabe, D.J. (2002) Species co-occurrence: a meta-analysis of J.M. Diamond's assembly rules model. Ecology, 83, 2091–2096.
Manly, B.F.J. (2006) Randomization, Bootstrap and Monte Carlo Methods in Biology (3rd edn). Chapman & Hall, Boca Raton.
Vaughan, I.P., Gotelli, N.J., Memmott, J., Pearson, C.E., Woodward, G. & Symondson, W.O.C. (2018) econullnetr: an R package using null models to analyse the structure of ecological networks and identify resource selection. Methods in Ecology and Evolution, 9, 728–733.
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set.seed(1234) sil.null <- generate_null_net(Silene[, 2:7], Silene.plants[, 2:6], sims = 10, c.samples = Silene[, 1], r.samples = Silene.plants[, 1]) # Network-level analysis net.stats <- bipartite_stats(sil.null, index.type = "networklevel", indices = c("linkage density", "weighted connectance", "weighted nestedness", "interaction evenness"), intereven = "sum") net.stats # Group-level analysis grp.stats <- bipartite_stats(sil.null, index.type = "grouplevel", indices = c("generality", "vulnerability", "partner diversity"), logbase = 2) grp.stats # Species-level statistics spp.stats <- bipartite_stats(sil.null, index.type = "specieslevel", indices = c("degree", "normalised degree", "partner diversity"), logbase = exp(1)) spp.stats # Show all data frames of results spp.stats$normalised.degree # Select one statistic spp.stats$normalised.degree$lower # Select one statistic at one level
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