(This article was first published on

Another grad student and I tried recently to make a contribution to our understanding of the relationship between ecological network structure (e.g., nestedness) and community structure (e.g., evenness)...**Recology**, and kindly contributed to R-bloggers)...Alas, I had no luck making new insights. However, I am providing the code used for this failed attempt in hopes that someone may find it useful. This is very basic code. It was roughly based off of the paper by Bluthgen et al. 2008 Ecology (here). In my code the number of interactions is set to 600, and there are 30 plant species, and 10 animal species. This assumes they share the same abundance distributions and sigma values.

#######################################################

###########Community-Network Structure Simulation######

#######################################################

library(bipartite)

# Set of mean and sd combinations of log-normal distribution

# In my case, these values were those empirically estimated

# from many bipartite networks from the Interaction Web Database

mu <- c(0.5,2.9,5.3)

sig <- c(0.75,1.6,2.45)

# Function to make a set of matrices based on some combination of mu and sigma values

make.matrices <- function(a,b,nmats){

plants <- round(rlnorm(n=30, meanlog=mu[a], sdlog=sig[b]))

animals <- round(rlnorm(n=10, meanlog=mu[a], sdlog=sig[b]))

plants <- plants*(600/sum(plants))

animals <- animals*(600/sum(animals))

r2dtable(nmats,animals,plants)

}

# Output matrices

matrices11 <- make.matrices(1,1,100)

matrices12 <- make.matrices(1,2,100)

matrices13 <- make.matrices(1,3,100)

matrices21 <- make.matrices(2,1,100)

matrices22 <- make.matrices(2,2,100)

matrices23 <- make.matrices(2,3,100)

matrices31 <- make.matrices(3,1,100)

matrices32 <- make.matrices(3,2,100)

matrices33 <- make.matrices(3,3,100)

# Calculate some network metrics-e.g., for one combination of mu and sigma

nest11 <- numeric(100)

linkspersp11 <- numeric(100)

webasymm11 <- numeric(100)

h211 <- numeric(100)

inteven11 <- numeric(100)

generality11 <- numeric(100)

for(i in 1:length(matrices11)){

m <- matrix(unlist(matrices11[i]),ncol=30,byrow=F)

metrics <- t(networklevel(m,index=c("nestedness","links per species","web asymmetry","H2","interaction evenness","generality")))

nest11[i] <- metrics[1]

linkspersp11[i] <- metrics[3]

webasymm11[i] <- metrics[4]

h211[i] <- metrics[5]

inteven11[i] <- metrics[6]

generality11[i] <- metrics[7]

}

nest11

linkspersp11

webasymm11

h211

inteven11

generality11

To

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