Calculate \(\gamma\), \(\beta\) and \(\alpha\) diversities of a metacommunity.
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
)A tibble with diversity values at each scale.
an object of class abundances.
a number: the order of diversity.
An estimator of diversity.
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.
a string containing one of the possible estimators of the probability distribution (see probabilities). Used only for extrapolation.
a string containing one of the possible unveiling methods to estimate the probabilities of the unobserved species (see probabilities). Used only for extrapolation.
an estimator of richness to evaluate the total number of species, see div_richness. used for interpolation and extrapolation.
the risk level, 5% by default, used to optimize the jackknife order.
the highest jackknife order allowed. Default is 10.
an estimator of sample coverage used by coverage.
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.
if TRUE, the function arguments are verified.
Should be set to FALSE to save time when the arguments have been checked elsewhere.
The function computes \(\gamma\) diversity after building a metacommunity from local communities according to their weight Marcon2014adivent. \(\alpha\) 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 \(\alpha\).