Tools for using null models to analyse ecological
networks (e.g. food webs, flower-visitation networks, seed-dispersal
networks) and detect resource preferences or non-random interactions among
network nodes. Tools are provided to run null models, test for and plot
preferences, plot and analyse bipartite networks, and export null model
results in a form compatible with other network analysis packages. The
underlying null model was developed by Agusti et al. (2003) Molecular
econullnetr is a package of functions for analysing ecological networks (e.g. food webs, flower-visitation networks) using null model analysis. The observed network is compared to networks generated by a null model, which assumes that the frequency with which species (nodes in the network) interact with one another is simply a consequence of their relative abundance (i.e. how often they encounter one another). Differences in the structure of observed and simulated networks (e.g. in the interaction strengths between nodes) implies that other mechanisms are involved in structuring the network.
econullnetr has six functions:
generate_null_net()to run null models.
test_interactions()for comparing observed and modelled interactions between species.
plot_preferences()for comparing a consumer species' observed and expected interactions with the resource species.
bipartite_stats()for calculating a broad set of statistics for bipartite networks to compare observed and modelled networks. This draws on the functions from the
plot_bipartite()which is a wrapper for the
plotweb()function, superimposing the null model results.
generate_edgelist()to export the null modelling results in a standard format for use with other R packages.
The package also includes three examples, each comprising multiple data sets. See the package vignette and help files for full descriptions of the package's functionality and data sets.
The following example runs a simple null model using one of the example data sets from
econullnetr and displays an example of
bipartite_stats() output and part of the output table for
test_interactions() (shortened for brevity). Full examples covering all of the main functionality are described in the help files and package vignette.
library(econullnetr)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], prog.count = FALSE)bipartite_stats(sil.null, index.type = "networklevel",indices = c("linkage density", "weighted connectance","interaction evenness"), intereven = "sum",prog.count = FALSE)
## Observed Null Lower.CL Upper.CL Test SES ## linkage density 5.0960242 6.8303241 6.4566289 7.1999193 Lower -6.740767 ## weighted connectance 0.1415562 0.1902469 0.1804936 0.1999978 Lower -7.412841 ## interaction evenness 0.8489881 0.8991247 0.8765239 0.9104503 Lower -4.482613
# First 10 rows of the output table for inter-specific (inter-node) interactions.# When running this code, two warnings will normally be generated to highlight# that: i) a very small number of model iterations was used for this example# and ii) there is a large number (155) of individual tests in the full# test_interactions table, so the risk of Type I errors needs to be considered.test_interactions(sil.null, 0.95)[1:10, ]
## Consumer Resource Observed Null Lower.95.CL Upper.95.CL Test SES ## 1 big.fly Achillea.millefolium 1 0.1 0 0.775 Stronger 2.8460499 ## 2 big.fly Hypericum.pulchrum 0 0.3 0 1.000 ns -0.6210590 ## 3 big.fly Papaver.rhoeas 0 0.0 0 0.000 ns NA ## 4 big.fly Senecio.jacobaea 0 0.6 0 1.000 ns -1.1618950 ## 5 big.fly Silene.gallica 0 0.0 0 0.000 ns NA ## 6 Bombus.pratorum Achillea.millefolium 0 0.0 0 0.000 ns NA ## 7 Bombus.pratorum Hypericum.pulchrum 0 0.1 0 0.775 ns -0.3162278 ## 8 Bombus.pratorum Papaver.rhoeas 0 0.0 0 0.000 ns NA ## 9 Bombus.pratorum Senecio.jacobaea 1 0.8 0 1.000 ns 0.4743416 ## 10 Bombus.pratorum Silene.gallica 0 0.1 0 0.775 ns -0.3162278
The development version of
econullnetr can be installed from GitHub: