div_gen_simpson: Generalized Simpson's Diversity

In divent: Entropy Partitioning to Measure Diversity

Generalized Simpson's Diversity

Description

Estimate the diversity sensu stricto, i.e. the effective number of species \insertCiteGrabchak2016divent from abundance or probability data.

Usage

div_gen_simpson(x, k = 1, ...)

## S3 method for class 'numeric'
div_gen_simpson(
  x,
  k = 1,
  estimator = c("Zhang", "naive"),
  as_numeric = FALSE,
  ...,
  check_arguments = TRUE
)

## S3 method for class 'species_distribution'
div_gen_simpson(
  x,
  k = 1,
  estimator = c("Zhang", "naive"),
  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.
k	the order of Hurlbert's diversity.
...	Unused.
estimator	An estimator of asymptotic diversity.
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.

Details

Bias correction requires the number of individuals.

Estimation techniques are from \insertCiteZhang2014;textualdivent. It is limited to orders k less than or equal to the number of individuals in the community.

Generalized Simpson's diversity cannot be estimated at a specified level of interpolation or extrapolation, and diversity partitioning is not available.

Value

A tibble with the site names, the estimators used and the estimated diversity.

References

\insertAllCited

See Also

ent_gen_simpson

Examples

# Diversity of each community
div_gen_simpson(paracou_6_abd, k = 50)

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