div_hurlbert: Hurlbert Diversity of a Community

In divent: Entropy Partitioning to Measure Diversity

Hurlbert Diversity of a Community

Description

Estimate the diversity sensu stricto, i.e. the effective number of species \insertCiteDauby2012;textualdivent from abundance or probability data.

Usage

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

## S3 method for class 'numeric'
div_hurlbert(
  x,
  k = 2,
  estimator = c("Hurlbert", "naive"),
  as_numeric = FALSE,
  ...,
  check_arguments = TRUE
)

## S3 method for class 'species_distribution'
div_hurlbert(
  x,
  k = 2,
  estimator = c("Hurlbert", "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

Several estimators are available to deal with incomplete sampling.

Bias correction requires the number of individuals.

Estimation techniques are from \insertCiteHurlbert1971;textualdivent.

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

Value

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

References

\insertAllCited

See Also

ent_hurlbert

Examples

# Diversity of each community
div_hurlbert(paracou_6_abd, k = 2)

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