mobsim (version 0.1.0)

sim_poisson_community: Simulate community with random spatial positions.

Description

This function simulates a community with a certain abundance distribution and and random spatial coordinates. This function consecutively calls sim_sad and sim_poisson_coords

Usage

sim_poisson_community(s_pool, n_sim, sad_type = "lnorm",
  sad_coef = list(cv_abund = 1), fix_s_sim = FALSE, xrange = c(0, 1),
  yrange = c(0, 1))

Arguments

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 (rlnorm); "geom" for the geometric distribution (rgeom), or "ls" for Fisher's log-series distribution (rls).

See the table in Details below, or rsad for all SAD model options.

sad_coef

List with named arguments to be passed to the distribution function defined by the argument sad_type. An overview of parameter names is given in the table below.

In mobsim the log-normal and the Poisson log-normal distributions can alternatively be parameterized by the coefficient of variation (cv) of the relative abundances in the species pool. Accordingly, cv_abund is the standard deviation of abundances divided by the mean abundance (no. of individuals / no. of species). cv_abund is thus negatively correlated with the evenness of the species abundance distribution.

Please note that the parameters mu and sigma are not equal to the mean and standard deviation of the log-normal distribution.

fix_s_sim

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

xrange

Extent of the community in x-direction (numeric vector of length 2)

yrange

Extent of the community in y-direction (numeric vector of length 2)

Value

A community object as defined by community.

Examples

# NOT RUN {
com1 <- sim_poisson_community(s_pool = 20, n_sim = 500, sad_type = "lnorm",
sad_coef = list("meanlog" = 2, "sdlog" = 1))
plot(com1)

# }