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Diversity partition

Source: R/div_part.R
div_part.Rd

Calculate \(\gamma\), \(\beta\) and \(\alpha\) diversities of a metacommunity.

Usage

div_part(
  abundances,
  q = 1,
  estimator = c("UnveilJ", "ChaoJost", "ChaoShen", "GenCov", "Grassberger", "Holste",
    "Marcon", "UnveilC", "UnveiliC", "ZhangGrabchak"),
  level = NULL,
  probability_estimator = c("Chao2015", "Chao2013", "ChaoShen", "naive"),
  unveiling = c("geometric", "uniform", "none"),
  richness_estimator = c("jackknife", "iChao1", "Chao1", "naive"),
  jack_alpha = 0.05,
  jack_max = 10,
  coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
  q_threshold = 10,
  check_arguments = TRUE
)

Arguments

abundances

an object of class abundances.

q

a number: the order of diversity.

estimator

An estimator of diversity.

level

the level of interpolation or extrapolation. It may be a sample size (an integer) or a sample coverage (a number between 0 and 1). If not NULL, the asymptotic estimator is ignored.

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.

richness_estimator

an estimator of richness to evaluate the total number of species, see div_richness. used for interpolation and 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.

q_threshold

the value of q above which diversity is computed directly with the naive estimator \((\sum{p_s^q}^{\frac{1}{(1-q)}}\), without computing entropy. When q is great, the exponential of entropy goes to \(0^{\frac{1}{(1-q)}}\), causing rounding errors while the naive estimator of diversity is less and less biased.

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 diversity values at each scale.

Details

The function computes \(\gamma\) diversity after building a metacommunity from local communities according to their weight (Marcon et al. 2014) . \(\alpha\) entropy is the weighted mean local entropy, converted into Hill numbers to obtain \(\alpha\) diversity. \(\beta\) diversity is obtained as the ratio of \(\gamma\) to \(\alpha\).

References

Marcon E, Scotti I, Hérault B, Rossi V, Lang G (2014). “Generalization of the Partitioning of Shannon Diversity.” Plos One, 9(3), e90289. doi:10.1371/journal.pone.0090289 .

Examples

div_part(paracou_6_abd)
#> # A tibble: 7 × 6
#>   site          scale     estimator order diversity weight
#>   <chr>         <chr>     <chr>     <dbl>     <dbl>  <dbl>
#> 1 Metacommunity gamma     "UnveilJ"     1    111.     6.25
#> 2 Metacommunity beta      ""            1      1.09  NA   
#> 3 Metacommunity alpha     ""            1    102.    NA   
#> 4 subplot_1     community "UnveilJ"     1     96.3    1.56
#> 5 subplot_2     community "UnveilJ"     1    113.     1.56
#> 6 subplot_3     community "UnveilJ"     1    105.     1.56
#> 7 subplot_4     community "UnveilJ"     1     94.6    1.56