Description Usage Arguments Details Value Author(s) Examples

View source: R/Sim_Community.R

Simulate species abundance distribution (SAD) of a local community with user-defined number of species and relative abundance distribution in the pool, and user-defined number of individuals in the simulated local community.

1 2 3 4 5 6 7 8 9 |

`s_pool` |
Number of species in the pool (integer) |

`n_sim` |
Number of individuals in the simulated community (integer) |

`sad_type` |
Root name of the species abundance distribution model of the
species pool (character) - e.g., "lnorm" for the lognormal distribution
( See the table in |

`sad_coef` |
List with named arguments to be passed to the distribution
function defined by the argument In Please note that the parameters |

`fix_s_sim` |
Should the simulation constrain the number of species in the simulated local community? (logical) |

`drop_zeros` |
Should the function remove species with abundance zero from the output? (logical) |

The function `sim_sad`

was built using code of the function
`rsad`

from the R package `sads`

. However, in
contrast to `rsad`

, the function `sim_sad`

allows to
define the number of individuals in the simulated local community. This is
implemented by converting the abundance distribution simulated based on
`rsad`

into a relative abundance distribution. This
relative abundance distribution is considered as the species pool for the
local community. In a second step the required no. of individuals `(n_sim)`

is sampled (with replacement) from this relative abundance distribution.

Please note that this might effect the interpretation of the parameters of
the underlying statistical distribution, e.g. the mean abundance will always
be `n_sim/s_pool`

irrespective of the settings of `sad_coef`

.

When `fix_s_sim = FALSE`

the species number in the local
community might deviate from `s_pool`

due to stochastic sampling. When
`fix_s_sim = TRUE`

the local number of species will equal
`s_pool`

, but this constraint can result in systematic biases from the
theoretical distribution parameters. Generally, with `fix_s_sim = TRUE`

additional very rare species will be added to the community, while the abundance
of the most common ones is reduced to keep the defined number of individuals.

Here is an overview of all available models (`sad_type`

) and their
respective coefficients (`sad_coef`

). Further information is provided
by the documentation of the specific functions that can be accesses by the
links. Please note that the coefficient `cv_abund`

for the log-normal
and Poisson log-normal model are only available within `mobsim`

.

SAD function | Distribution name | coef #1 | coef #2 | coef #3 |

`rbs` | Mac-Arthur's brokenstick | N | S | |

`rgamma` | Gamma distribution | shape | rate | scale |

`rgeom` | Geometric distribution | prob | ||

`rlnorm` | Log-normal distributions | meanlog | sdlog | cv_abund |

`rls` | Fisher's log-series distribution | N | alpha | |

`rmzsm` | Metacommunity zero-sum multinomial | J | theta | |

`rnbinom` | Negative binomial distribution | size | prob | mu |

`rpareto` | Pareto distribution | shape | scale | |

`rpoilog` | Poisson-lognormal distribution | mu | sigma | cv_abund |

`rpower` | Power discrete distributions | s | ||

`rpowbend` | Puyeo's Power-bend discrete distribution | s | omega | |

`rweibull` | Weibull distribution | shape | scale | |

Object of class `sad`

, which contains a named integer vector
with species abundances

Felix May

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | ```
#Simulate log-normal species abundance distribution
sad_lnorm1 <- sim_sad(s_pool = 100, n_sim = 10000, sad_type = "lnorm",
sad_coef = list("meanlog" = 5, "sdlog" = 0.5))
plot(sad_lnorm1, method = "octave")
plot(sad_lnorm1, method = "rank")
# Alternative parameterization of the log-normal distribution
sad_lnorm2 <- sim_sad(s_pool = 100, n_sim = 10000, sad_type = "lnorm",
sad_coef = list("cv_abund" = 0.5))
plot(sad_lnorm2, method = "octave")
# Fix species richness in the simulation by adding rare species
sad_lnorm3a <- sim_sad(s_pool = 500, n_sim = 10000, sad_type = "lnorm",
sad_coef = list("cv_abund" = 5), fix_s_sim = TRUE)
sad_lnorm3b <- sim_sad(s_pool = 500, n_sim = 10000, sad_type = "lnorm",
sad_coef = list("cv_abund" = 5))
plot(sad_lnorm3a, method = "rank")
points(1:length(sad_lnorm3b), sad_lnorm3b, type = "b", col = 2)
legend("topright", c("fix_s_sim = TRUE","fix_s_sim = FALSE"),
col = 1:2, pch = 1)
# Different important SAD models
# Fisher's log-series
sad_logseries <- sim_sad(s_pool = NULL, n_sim = 10000, sad_type = "ls",
sad_coef = list("N" = 1e5, "alpha" = 20))
# Poisson log-normal
sad_poilog <- sim_sad(s_pool = 100, n_sim = 10000, sad_type = "poilog",
sad_coef = list("mu" = 5, "sig" = 0.5))
# Mac-Arthur's broken stick
sad_broken_stick <- sim_sad(s_pool = NULL, n_sim = 10000, sad_type = "bs",
sad_coef = list("N" = 1e5, "S" = 100))
# Plot all SADs together as rank-abundance curves
plot(sad_logseries, method = "rank")
lines(1:length(sad_lnorm2), sad_lnorm2, type = "b", col = 2)
lines(1:length(sad_poilog), sad_poilog, type = "b", col = 3)
lines(1:length(sad_broken_stick), sad_broken_stick, type = "b", col = 4)
legend("topright", c("Log-series","Log-normal","Poisson log-normal","Broken stick"),
col = 1:4, pch = 1)
``` |

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