rcommunity: Random communities

In divent: Entropy Partitioning to Measure Diversity

Random communities

Description

rcommunity() draws random communities according to a probability distribution. rspcommunity() extends it by spatializing the random communities.

Usage

rcommunity(
  n,
  size = sum(abd),
  prob = NULL,
  abd = NULL,
  bootstrap = c("Chao2015", "Marcon2012", "Chao2013"),
  species_number = 300,
  distribution = c("lnorm", "lseries", "geom", "bstick"),
  sd_lnorm = 1,
  prob_geom = 0.1,
  fisher_alpha = 40,
  coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
  check_arguments = TRUE
)

rspcommunity(
  n,
  size = sum(abd),
  prob = NULL,
  abd = NULL,
  bootstrap = c("Chao2015", "Marcon2012", "Chao2013"),
  species_number = 300,
  distribution = c("lnorm", "lseries", "geom", "bstick"),
  sd_lnorm = 1,
  prob_geom = 0.1,
  fisher_alpha = 40,
  coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
  spatial = c("Binomial", "Thomas"),
  thomas_scale = 0.2,
  thomas_mu = 10,
  win = spatstat.geom::owin(),
  species_names = NULL,
  weight_distribution = c("Uniform", "Weibull", "Exponential"),
  w_min = 1,
  w_max = 1,
  w_mean = 15,
  weibull_scale = 20,
  weibull_shape = 2,
  check_arguments = TRUE
)

Arguments

n	the number of communities to draw.
size	the number of individuals to draw in each community.
prob	a numeric vector containing probabilities.
abd	a numeric vector containing abundances.
bootstrap	the method used to obtain the probabilities to generate bootstrapped communities from observed abundances. If "Marcon2012", the probabilities are simply the abundances divided by the total number of individuals \insertCiteMarcon2012adivent. If "Chao2013" or "Chao2015" (by default), a more sophisticated approach is used (see as_probabilities) following \insertCiteChao2013;textualdivent or \insertCiteChao2015;textualdivent.
species_number	the number of species.
distribution	The distribution of species abundances. May be "lnorm" (log-normal), "lseries" (log-series), "geom" (geometric) or "bstick" (broken stick).
sd_lnorm	the simulated log-normal distribution standard deviation. This is the standard deviation on the log scale.
prob_geom	the proportion of resources taken by successive species of the geometric distribution.
fisher_alpha	Fisher's \alpha in the log-series distribution.
coverage_estimator	an estimator of sample coverage used by coverage.
check_arguments	if TRUE, the function arguments are verified. Should be set to FALSE to save time when the arguments have been checked elsewhere.
spatial	the spatial distribution of points. May be "Binomial" (a completely random point pattern except for its fixed number of points) or "Thomas" for a clustered point pattern with parameters scale and mu.
thomas_scale	in Thomas point patterns, the standard deviation of random displacement (along each coordinate axis) of a point from its cluster center.
thomas_mu	in Thomas point patterns, the mean number of points per cluster. The intensity of the Poisson process of cluster centers is calculated as the number of points (size) per area divided by thomas_mu.
win	the window containing the point pattern. It is an spatstat.geom::owin object. Default is a 1x1 square.
species_names	a vector of characters or of factors containing the possible species names.
weight_distribution	the distribution of point weights. By default, all weight are 1. May be "uniform" for a uniform distribution between w_min and w_max, "weibull" with parameters w_min, weibull_scale and shape or "exponential" with parameter w_mean.
w_min	the minimum weight in a uniform, exponential or Weibull distribution.
w_max	the maximum weight in a uniform distribution.
w_mean	the mean weight in an exponential distribution (i.e. the negative of the inverse of the decay rate).
weibull_scale	the scale parameter in a Weibull distribution.
weibull_shape	the shape parameter in a Weibull distribution.

Details

Communities of fixed size are drawn in a multinomial distribution according to the distribution of probabilities provided by prob. An abundance vector abd may be used instead of probabilities, then size is by default the total number of individuals in the vector. Random communities can be built by drawing in a multinomial law following \insertCiteMarcon2012a;textualdivent, or trying to estimate the distribution of the actual community with probabilities. If bootstrap is "Chao2013", the distribution is estimated by a single parameter model and unobserved species are given equal probabilities. If bootstrap is "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 lognormal ("lnorm") \insertCitePreston1948divent with given standard deviation (sd_lnorm; note that the mean is actually a normalizing constant. Its value is set equal to 0 for the simulation of the normal distribution of unnormalized log-abundances), log-series ("lseries") \insertCiteFisher1943divent with parameter fisher_alpha, geometric ("geom") \insertCiteMotomura1932divent with parameter prob_geom, or broken stick ("bstick") \insertCiteMacArthur1957divent. The number of simulated species is fixed by species_number, except for "lseries" where it is obtained from fisher_alpha and size: S = \alpha \ln(1 + size / \alpha). Note that the probabilities are drawn once only. If the number of communities to draw, n, is greater than 1, then they are drawn in a multinomial distribution following these probabilities.

Log-normal, log-series and broken-stick distributions are stochastic. The geometric distribution is completely determined by its parameters.

Spatialized communities include the location of individuals in a window, in a dbmss::wmppp object. Several point processes are available, namely binomial (points are uniformly distributed in the window) and \insertCiteThomas1949;textualdivent, which is clustered.

Point weights, that may be for instance the size of the trees in a forest community, can be uniform, follow a Weibull or a negative exponential distribution. The latter describe well the diameter distribution of trees in a forest \insertCiteRennolls1985,Turner2004divent.

Value

rcommunity() returns an object of class abundances.

rspcommunity() returns either a spatialized community, which is a dbmss::wmppp object , with PointType values as species names if n=1 or an object of class ppplist (see spatstat.geom::solist) if n>1.

References

\insertAllCited

Examples

# Generate a community made of 100000 individuals among 300 species and fit it
abundances <- rcommunity(n = 1, size = 1E5,
  species_number = 300, distribution = "lnorm")
autoplot(abundances)
X <- rspcommunity(1, size = 30, species_number = 5)
autoplot(X)

divent documentation built on April 3, 2025, 7:40 p.m.