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)
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.gamma
and FDis.mean
can be used to explore the set concavity criterion (Ricotta 2005) for FDis.
A graph plotting the results of the simulations is also returned.
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 tr.method
.
logical; should FDis, FEve, FDiv, and Rao's quadratic entropy (Q) be weighted by species abundances?
Etienne Laliberté etiennelaliberte@gmail.com https://www.elaliberte.info/
The simulations performed by simul.dbFD
can take several hours if length(s)
and/or 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 simul.dbFD
# this should take just a few minutes
if (FALSE) {
ex1 <- simul.dbFD(s = c(10, 20, 30, 40, 50), r = 5)
ex1
}
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