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 seven 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 bipartite
package.plot_bipartite() which is a wrapper for the
bipartite package’s 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.expand_matrix() to convert summarised interaction
matrices (e.g. as used by the bipartite package) into a
format suitable for generate_null_net.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)
# Network-level statistics
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 -0.3162278The development version of econullnetr can be installed
from GitHub:
# install.packages("devtools")
devtools::install_github("ivaughan/econullnetr")