Similarity-Based Entropy of a Community — ent

Estimate the entropy 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.

Usage

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

# S3 method for class 'numeric'
ent_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'
ent_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,
  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. 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: a number: the order of diversity.
...: Unused.
estimator: An estimator of entropy.
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 entropy.

Details

All species of the species_distribution must be found in the matrix of similarities if it is named. If it is not or if x is numeric, 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 or its numeric equivalent.

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

Examples

# Similarity matrix
Z <- fun_similarity(paracou_6_fundist)
# Diversity of each community
ent_similarity(paracou_6_abd, similarities = Z, q = 2)
#> # A tibble: 4 × 5
#>   site      weight estimator order entropy
#>   <chr>      <dbl> <chr>     <dbl>   <dbl>
#> 1 subplot_1   1.56 UnveilJ       2   0.234
#> 2 subplot_2   1.56 UnveilJ       2   0.248
#> 3 subplot_3   1.56 UnveilJ       2   0.245
#> 4 subplot_4   1.56 UnveilJ       2   0.228
# gamma diversity
ent_similarity(paracou_6_abd, similarities = Z, q = 2, gamma = TRUE)
#> # A tibble: 1 × 4
#>   site          estimator order entropy
#>   <chr>         <chr>     <dbl>   <dbl>
#> 1 Metacommunity UnveilJ       2   0.237