div_gen_simpson {divent} | R Documentation |
Generalized Simpson's Diversity
Description
Estimate the diversity sensu stricto, i.e. the effective number of species (Grabchak et al. 2017) 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 |
check_arguments |
if |
Details
Bias correction requires the number of individuals.
Estimation techniques are from Zhang and Grabchak (2016).
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
Grabchak M, Marcon E, Lang G, Zhang Z (2017).
“The Generalized Simpson's Entropy Is a Measure of Biodiversity.”
Plos One, 12(3), e0173305.
doi:10.1371/journal.pone.0173305.
Zhang Z, Grabchak M (2016).
“Entropic Representation and Estimation of Diversity Indices.”
Journal of Nonparametric Statistics, 28(3), 563–575.
doi:10.1080/10485252.2016.1190357.
See Also
Examples
# Diversity of each community
div_gen_simpson(paracou_6_abd, k = 50)