Hurlbert Diversity of a Community

Estimate the diversity sensu stricto, i.e. the effective number of species Dauby and Hardy (2012) 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.

Value

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

Details

Several estimators are available to deal with incomplete sampling.

Bias correction requires the number of individuals.

Estimation techniques are from Hurlbert (1971) .

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

References

Dauby G, Hardy OJ (2012). “Sampled-Based Estimation of Diversity Sensu Stricto by Transforming Hurlbert Diversities into Effective Number of Species.” Ecography, 35(7), 661–672. doi:10.1111/j.1600-0587.2011.06860.x .

Hurlbert SH (1971). “The Nonconcept of Species Diversity: A Critique and Alternative Parameters.” Ecology, 52(4), 577–586. doi:10.2307/1934145 .

See also

ent_hurlbert

Examples

# Diversity of each community
div_hurlbert(paracou_6_abd, k = 2)
#> # A tibble: 4 × 5
#>   site      weight estimator order diversity
#>   <chr>      <dbl> <chr>     <dbl>     <dbl>
#> 1 subplot_1   1.56 Hurlbert      2      42.3
#> 2 subplot_2   1.56 Hurlbert      2      44.6
#> 3 subplot_3   1.56 Hurlbert      2      48.9
#> 4 subplot_4   1.56 Hurlbert      2      36.0