div_richness: Number of Species of a Community

In divent: Entropy Partitioning to Measure Diversity

Number of Species of a Community

Description

Estimate the number of species from abundance or probability data. Several estimators are available to deal with incomplete sampling.

Usage

div_richness(x, ...)

## S3 method for class 'numeric'
div_richness(
  x,
  estimator = c("jackknife", "iChao1", "Chao1", "rarefy", "naive"),
  jack_alpha = 0.05,
  jack_max = 10,
  level = NULL,
  probability_estimator = c("Chao2015", "Chao2013", "ChaoShen", "naive"),
  unveiling = c("geometric", "uniform", "none"),
  coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
  as_numeric = FALSE,
  ...,
  check_arguments = TRUE
)

## S3 method for class 'species_distribution'
div_richness(
  x,
  estimator = c("jackknife", "iChao1", "Chao1", "rarefy", "naive"),
  jack_alpha = 0.05,
  jack_max = 10,
  level = NULL,
  probability_estimator = c("Chao2015", "Chao2013", "ChaoShen", "naive"),
  unveiling = c("geometric", "uniform", "none"),
  coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
  gamma = FALSE,
  as_numeric = FALSE,
  ...,
  check_arguments = TRUE
)

Arguments

x	An object, that may be a numeric vector containing abundances or probabilities, or an object of class abundances or probabilities.
...	Unused. The metacommunity if built by combining the community abundances with respect to their weight.
estimator	An estimator of richness to evaluate the total number of species.
jack_alpha	the risk level, 5% by default, used to optimize the jackknife order.
jack_max	the highest jackknife order allowed. Default is 10.
level	The level of interpolation or extrapolation. It may be a sample size (an integer) or a sample coverage (a number between 0 and 1). The asymptotic estimator is used in extrapolation (i.e. a level greater than the sample size).
probability_estimator	A string containing one of the possible estimators of the probability distribution (see probabilities). Used only by the estimator of richness "rarefy".
unveiling	A string containing one of the possible unveiling methods to estimate the probabilities of the unobserved species (see probabilities). Used only by the estimator of richness "rarefy".
coverage_estimator	an estimator of sample coverage used by coverage.
as_numeric	if TRUE, a number or a numeric vector is returned rather than a tibble.
check_arguments	if TRUE, the function arguments are verified. Should be set to FALSE to save time when the arguments have been checked elsewhere.
gamma	if TRUE, \gamma diversity, i.e. diversity of the metacommunity, is computed.

Details

Bias correction requires the number of individuals. Chao's estimation techniques are from \insertCiteChao2014;textualdivent and \insertCiteChiu2014a;textualdivent. The Jackknife estimator is calculated by a straight adaptation of the code by Ji-Ping Wang (jackknife in package SPECIES). The optimal order is selected according to \insertCiteBurnham1978,Burnham1979;textualdivent. Many other estimators are available elsewhere, the ones implemented here are necessary for other entropy estimations.

Richness can be estimated at a specified level of interpolation or extrapolation, either a chosen sample size or sample coverage \insertCiteChiu2014adivent, rather than its asymptotic value. Extrapolation relies on the estimation of the asymptotic richness. If probability_estimator is "naive", then the asymptotic estimation of richness is made using the chosen estimator, else the asymptotic distribution of the community is derived and its estimated richness adjusted so that the richness of a sample of this distribution of the size of the actual sample has the richness of the actual sample.

Value

A tibble with the site names, the estimators used and the estimated numbers of species.

References

\insertAllCited

Examples

# Diversity of each community
div_richness(paracou_6_abd)
# gamma diversity
div_richness(paracou_6_abd, gamma = TRUE)

# At 80% coverage
div_richness(paracou_6_abd, level = 0.8)

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