div_richness {divent} | R Documentation |
Number of Species of a Community
Description
Estimate the number of species from abundance or probability data. Several estimators are available to deal with incomplete sampling.
Usage
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
)
Arguments
x |
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. |
estimator |
An estimator of richness to evaluate the total number of species. |
jack_alpha |
The risk level, 5% by default, used to optimize the jackknife order. |
jack_max |
The highest jackknife order allowed. Default is 10. |
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).
The asymptotic |
probability_estimator |
A string containing one of the possible estimators of the probability distribution (see probabilities). Used only by the estimator of richness "rarefy". |
unveiling |
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". |
coverage_estimator |
An estimator of sample coverage used by coverage. |
as_numeric |
If |
check_arguments |
If |
gamma |
If |
Details
Bias correction requires the number of individuals. Chao's correction techniques are from Chao et al. (2014) and Chiu et al. (2014). 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 Burnham and Overton (1978); Burnham and Overton (1979). 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
(Chiu et al. 2014), 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.
Value
A tibble with the site names, the estimators used and the estimated numbers of species.
References
Burnham KP, Overton WS (1978).
“Estimation of the Size of a Closed Population When Capture Probabilities Vary among Animals.”
Biometrika, 65(3), 625–633.
doi:10.2307/2335915.
Burnham KP, Overton WS (1979).
“Robust Estimation of Population Size When Capture Probabilities Vary among Animals.”
Ecology, 60(5), 927–936.
doi:10.2307/1936861.
Chao A, Gotelli NJ, Hsieh TC, Sander EL, Ma KH, Colwell RK, Ellison AM (2014).
“Rarefaction and Extrapolation with Hill Numbers: A Framework for Sampling and Estimation in Species Diversity Studies.”
Ecological Monographs, 84(1), 45–67.
doi:10.1890/13-0133.1.
Chiu C, Wang Y, Walther BA, Chao A (2014).
“An Improved Nonparametric Lower Bound of Species Richness via a Modified Good-Turing Frequency Formula.”
Biometrics, 70(3), 671–682.
doi:10.1111/biom.12200, 24945937.
Examples
# Diversity of each community
div_richness(paracou_6_abd)
# gamma diversity
div_richness(paracou_6_abd, gamma = TRUE)
# At 80% coverage
div_richness(paracou_6_abd, level = 0.8)