Hurlbert Diversity of a Community — div

Estimate the diversity sensu stricto, i.e. the effective number of species number of species Dauby and Hardy (2012) from abundance or probability data.

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

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

# S3 method for species_distribution
div_hurlbert(
  x,
  k = 2,
  estimator = c("Hurlbert", "naive"),
  ...,
  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 .

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