rCommunity(n, size = sum(NorP), NorP = 1, BootstrapMethod = "Chao2015", S = 300, Distribution = "lnorm", sd = 1, prob = 0.1, alpha = 40, CheckArguments = TRUE)"Marcon", the probabilities are simply the abundances divided by the total number of individuals (Marcon et al., 2012). If "Chao2013" or "Chao2015" (by default), a more sophisticated approach is used (see as.ProbaVector) following Chao et al. (2013) or Chao et al. (2015).
"lnorm" (log-normal), "lseries" (log-series), "geom" (geometric) or "bstick" (broken stick).
TRUE, the function arguments are verified. Should be set to FALSE to save time when the arguments have been checked elsewhere.
AbdVector) if a single community has been drawn, or a MetaCommunity containing simulated communities.
size are drawn in a multinomial distribution according to the distribution of probabilities provided by NorP.
An abundance vector may be used instead of probabilities, then size is by default the total number of individuals in the vector. Random communities are built by drawing in a multinomial law following Marcon et al. (2012), or trying to estimate the distribution of the actual community with as.ProbaVector. If BootstrapMethod = "Chao2013", the distribution is estimated by a single parameter model and unobserved species are given equal probabilities. If BootstrapMethod = "Chao2015", a two-parameter model is used and unobserved species follow a geometric distribution.
Alternatively, the probabilities may be drawn following a classical distribution: either a lognormal ("lnorm") one (Preston, 1948) with given standard deviation (sd; note that the mean is actually a normalizing constant. Its values is set equal to 0 for the simulation of the normal distribution of unnormalized log-abundances), a log-series ("lseries") one (Fisher et al., 1943) with parameter alpha, a geometric ("geom") one (Motomura, 1932) with parameter prob, or a broken stick ("bstick") one (MacArthur, 1957). The number of simulated species is fixed by S, except for "lseries" where it is obtained from alpha and size: $S=\alpha \ln(1 + \frac{size}{\alpha})$.
Log-normal, log-series and broken-stick distributions are stochastic. The geometric distribution is completely determined by its parameters.
Chao, A., Hsieh, T. C., Chazdon, R. L., Colwell, R. K., Gotelli, N. J. (2015) Unveiling the Species-Rank Abundance Distribution by Generalizing Good-Turing Sample Coverage Theory. Ecology 96(5): 1189-1201.
Fisher R.A., Corbet A.S., Williams C.B. (1943) The Relation Between the Number of Species and the Number of Individuals in a Random Sample of an Animal Population. Journal of Animal Ecology 12: 42-58. MacArthur R.H. (1957) On the Relative Abundance of Bird Species. PNAS 43(3): 293-295.
Marcon, E., Herault, B., Baraloto, C. and Lang, G. (2012). The Decomposition of Shannon's Entropy and a Confidence Interval for Beta Diversity. Oikos 121(4): 516-522. Motomura I. (1932) On the statistical treatment of communities. Zoological Magazine 44: 379-383. Preston, F.W. (1948). The commonness, and rarity, of species. Ecology 29(3): 254-283. Reese G. C., Wilson K. R., Flather C. H. (2013) Program SimAssem: Software for simulating species assemblages and estimating species richness. Methods in Ecology and Evolution 4: 891-896.
SpeciesDistribution and the program SimAssem (Reese et al., 2013; not an R package) for more distributions.
# Generate communities made of 100000 individuals among 300 species and fit them
par(mfrow = c(2,2))
for (d in c("lnorm", "lseries", "geom", "bstick")) {
rCommunity(n = 1, size = 1E5, S = 300, Distribution = d) -> AbdVec
plot(AbdVec, Distribution = d, main = d)
}
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