rCommunity {entropart} | R Documentation |

## Random Communities

### Description

Draws random communities according to a probability distribution.

### Usage

```
rCommunity(n, size = sum(NorP), NorP = 1, BootstrapMethod = "Chao2015", S = 300,
Distribution = "lnorm", sd = 1, prob = 0.1, alpha = 40,
CheckArguments = TRUE)
```

### Arguments

`n` |
The number of communities to draw. |

`size` |
The number of individuals to draw in each community. |

`BootstrapMethod` |
The method used to obtain the probabilities to generate bootstrapped communities from observed abundances. If |

`NorP` |
A numeric vector. Contains either abundances or probabilities. |

`S` |
The number of species. |

`Distribution` |
The distribution of species frequencies. May be |

`sd` |
The simulated distribution standard deviation. For the log-normal distribution, this is the standard deviation on the log scale. |

`prob` |
The proportion of ressources taken by successive species. |

`alpha` |
Fisher's alpha. |

`CheckArguments` |
Logical; if |

### Details

Communities of fixed `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.

### Value

A vector of species abundances (`AbdVector`

) if a single community has been drawn, or a `MetaCommunity`

containing simulated communities.

### References

Chao, A., Wang, Y. T. and Jost, L. (2013). Entropy and the species accumulation curve: a novel entropy estimator via discovery rates of new species. *Methods in Ecology and Evolution* 4(11): 1091-1100.

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.

### See Also

`SpeciesDistribution`

and the program `SimAssem`

(Reese *et al.*, 2013; not an R package) for more distributions.

### Examples

```
# 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)
}
```

*entropart*version 1.6-13 Index]