Simulations to Explore Relationships Between Functional Diversity Indices
simul.dbFD generates artificial communities of species with artificial functional traits. Different functional diversity (FD) indices are computed from these communities using
dbFD to explore their inter-relationships.
simul.dbFD(s = c(5, 10, 15, 20, 25, 30, 35, 40), t = 3, r = 10, p = 100, tr.method = c("unif", "norm", "lnorm"), abun.method = c("lnorm", "norm", "unif"), w.abun = TRUE)
- vector listing the different levels of species richness used in the simulations
- number of traits
- number of replicates per species richness level
- number of species in the common species pool
- character string indicating the sampling distribution for the traits.
"unif"is a uniform distribution,
"norm"is a normal distribution, and
"lnorm"is a lognormal distribution.
- character string indicating the sampling distribution for the species abundances. Same as for
- logical; should FDis, FEve, FDiv, and Rao's quadratic entropy (Q) be weighted by species abundances?
A list contaning the following elements:
- data frame containing the results of the simulations
- matrix containing the traits
- matrix containing the abundances
- species abundances from the pooled set of communities
- FDis of the pooled set of communities
- mean FDis from all communities
FDis.meancan be used to explore the set concavity criterion (Ricotta 2005) for FDis.A graph plotting the results of the simulations is also returned.
The simulations performed by
simul.dbFD can take several hours if
r is large. Run a test with the default parameters first.
Laliberté, E. and P. Legendre (2010) A distance-based framework for measuring functional diversity from multiple traits. Ecology 91299:305.
Ricotta, C. (2005) A note on functional diversity measures. Basic and Applied Ecology 6:479-486.
dbFD, the function called in
# this should take just a few minutes ## Not run: # ex1 <- simul.dbFD(s = c(10, 20, 30, 40, 50), r = 5) # ex1 # ## End(Not run)