Estimate the number of species from abundance or probability data. Several estimators are available to deal with incomplete sampling.
div_richness(x, ...)
# S3 method for class 'numeric'
div_richness(
x,
estimator = c("jackknife", "iChao1", "Chao1", "rarefy", "naive"),
jack_alpha = 0.05,
jack_max = 10,
level = NULL,
probability_estimator = c("Chao2015", "Chao2013", "ChaoShen", "naive"),
unveiling = c("geometric", "uniform", "none"),
coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
as_numeric = FALSE,
...,
check_arguments = TRUE
)
# S3 method for class 'species_distribution'
div_richness(
x,
estimator = c("jackknife", "iChao1", "Chao1", "rarefy", "naive"),
jack_alpha = 0.05,
jack_max = 10,
level = NULL,
probability_estimator = c("Chao2015", "Chao2013", "ChaoShen", "naive"),
unveiling = c("geometric", "uniform", "none"),
coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
gamma = FALSE,
...,
check_arguments = TRUE
)An object, that may be a numeric vector containing abundances or probabilities, or an object of class abundances or probabilities.
Unused. The metacommunity if built by combining the community abundances with respect to their weight.
An estimator of richness to evaluate the total number of species.
The risk level, 5% by default, used to optimize the jackknife order.
The highest jackknife order allowed. Default is 10.
The level of interpolation or extrapolation.
It may be a sample size (an integer) or a sample coverage
(a number between 0 and 1).
The asymptotic estimator is used in extrapolation
(i.e. a level greater than the sample size).
A string containing one of the possible estimators of the probability distribution (see probabilities). Used only by the estimator of richness "rarefy".
A string containing one of the possible unveiling methods to estimate the probabilities of the unobserved species (see probabilities). Used only by the estimator of richness "rarefy".
An estimator of sample coverage used by coverage.
If TRUE, a number or a numeric vector is returned rather than a tibble.
If TRUE, the function arguments are verified.
Should be set to FALSE to save time when the arguments have been checked elsewhere.
If TRUE, \(\gamma\) diversity, i.e. diversity of the metacommunity, is computed.
A tibble with the site names, the estimators used and the estimated numbers of species.
Bias correction requires the number of individuals. Chao's correction techniques are from Chao2014;textualdivent and Chiu2014a;textualdivent. The Jackknife estimator is calculated by a straight adaptation of the code by Ji-Ping Wang (jackknife in package SPECIES). The optimal order is selected according to Burnham1978,Burnham1979;textualdivent. Many other estimators are available elsewhere, the ones implemented here are necessary for other entropy estimations.
Richness can be estimated at a specified level of interpolation or
extrapolation, either a chosen sample size or sample coverage
Chiu2014adivent, rather than its asymptotic value.
Extrapolation relies on the estimation of the asymptotic richness.
If probability_estimator is "naive", then the asymptotic estimation of
richness is made using the chosen estimator, else the asymptotic
distribution of the community is derived and its estimated richness adjusted
so that the richness of a sample of this distribution of the size of the
actual sample has the richness of the actual sample.
# Diversity of each community
div_richness(paracou_6_abd)
#> # A tibble: 4 × 5
#> site weight estimator order diversity
#> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 subplot_1 1.56 Jackknife 3 0 355
#> 2 subplot_2 1.56 Jackknife 2 0 348
#> 3 subplot_3 1.56 Jackknife 2 0 315
#> 4 subplot_4 1.56 Jackknife 2 0 296
# gamma diversity
div_richness(paracou_6_abd, gamma = TRUE)
#> # A tibble: 1 × 4
#> site estimator order diversity
#> <chr> <chr> <dbl> <dbl>
#> 1 Metacommunity Jackknife 2 0 473
# At 80% coverage
div_richness(paracou_6_abd, level = 0.8)
#> # A tibble: 4 × 6
#> site weight estimator order level diversity
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 subplot_1 1.56 SAC 0 304 106.
#> 2 subplot_2 1.56 SAC 0 347 125.
#> 3 subplot_3 1.56 SAC 0 333 117.
#> 4 subplot_4 1.56 SAC 0 303 109.