Estimate the diversity of species from abundance or probability data and a similarity matrix between species. Several estimators are available to deal with incomplete sampling. Bias correction requires the number of individuals.

div_similarity(x, similarities, q = 1, ...)

# S3 method for class 'numeric'
div_similarity(
  x,
  similarities = diag(length(x)),
  q = 1,
  estimator = c("UnveilJ", "Max", "ChaoShen", "MarconZhang", "UnveilC", "UnveiliC",
    "naive"),
  probability_estimator = c("Chao2015", "Chao2013", "ChaoShen", "naive"),
  unveiling = c("geometric", "uniform", "none"),
  jack_alpha = 0.05,
  jack_max = 10,
  coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
  sample_coverage = NULL,
  as_numeric = FALSE,
  ...,
  check_arguments = TRUE
)

# S3 method for class 'species_distribution'
div_similarity(
  x,
  similarities = diag(sum(!colnames(x) %in% non_species_columns)),
  q = 1,
  estimator = c("UnveilJ", "Max", "ChaoShen", "MarconZhang", "UnveilC", "UnveiliC",
    "naive"),
  probability_estimator = c("Chao2015", "Chao2013", "ChaoShen", "naive"),
  unveiling = c("geometric", "uniform", "none"),
  jack_alpha = 0.05,
  jack_max = 10,
  coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
  gamma = 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. If it is a numeric vector, then its length must equal the dimensions of the similarities matrix: species are assumed to be in the same order.

similarities

A similarity matrix, that can be obtained by fun_similarity. Its default value is the identity matrix.

q

The order of diversity.

...

Unused.

estimator

An estimator of asymptotic diversity.

probability_estimator

A string containing one of the possible estimators of the probability distribution (see probabilities). Used only for extrapolation.

unveiling

A string containing one of the possible unveiling methods to estimate the probabilities of the unobserved species (see probabilities). Used only for extrapolation.

jack_alpha

The risk level, 5% by default, used to optimize the jackknife order.

jack_max

The highest jackknife order allowed. Default is 10.

coverage_estimator

An estimator of sample coverage used by coverage.

sample_coverage

The sample coverage of x calculated elsewhere. Used to calculate the gamma diversity of meta-communities, see details.

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.

gamma

If TRUE, \(\gamma\) diversity, i.e. diversity of the metacommunity, is computed.

Value

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

Details

All species of the species_distribution must be found in the matrix of similarities if it is named. If it is not, its size must equal the number of species. Then, the order of species is assumed to be the same as that of the species_distribution.

Similarity-Based diversity can't be interpolated of extrapolated as of the state of the art.

References

Examples

# Similarity matrix
Z <- fun_similarity(paracou_6_fundist)
# Diversity of each community
div_similarity(paracou_6_abd, similarities = Z, q = 2)
#> # A tibble: 4 × 5
#>   site      weight estimator order diversity
#>   <chr>      <dbl> <chr>     <dbl>     <dbl>
#> 1 subplot_1   1.56 UnveilJ       2      1.31
#> 2 subplot_2   1.56 UnveilJ       2      1.33
#> 3 subplot_3   1.56 UnveilJ       2      1.32
#> 4 subplot_4   1.56 UnveilJ       2      1.30
# gamma diversity
div_similarity(paracou_6_abd, similarities = Z, q = 2, gamma = TRUE)
#> # A tibble: 4 × 5
#>   site      weight estimator order diversity
#>   <chr>      <dbl> <chr>     <dbl>     <dbl>
#> 1 subplot_1   1.56 UnveilJ       2      1.31
#> 2 subplot_2   1.56 UnveilJ       2      1.33
#> 3 subplot_3   1.56 UnveilJ       2      1.32
#> 4 subplot_4   1.56 UnveilJ       2      1.30