Estimate the diversity sensu stricto, i.e. the Hill (1973) number of species from abundance or probability data.
div_hill(x, q = 1, ...)
# S3 method for numeric
div_hill(
x,
q = 1,
estimator = c("UnveilJ", "ChaoJost", "ChaoShen", "GenCov", "Grassberger", "Marcon",
"UnveilC", "UnveiliC", "ZhangGrabchak", "naive", "Bonachela", "Holste"),
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"),
sample_coverage = NULL,
as_numeric = FALSE,
...,
check_arguments = TRUE
)
# S3 method for species_distribution
div_hill(
x,
q = 1,
estimator = c("UnveilJ", "ChaoJost", "ChaoShen", "GenCov", "Grassberger", "Marcon",
"UnveilC", "UnveiliC", "ZhangGrabchak", "naive", "Bonachela", "Holste"),
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"),
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.
The order of diversity.
Unused.
An estimator of asymptotic 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 sample coverage of x calculated elsewhere.
Used to calculate the gamma diversity of meta-communities, see details.
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 diversity.
Several estimators are available to deal with incomplete sampling.
Bias correction requires the number of individuals.
Estimation techniques are from Chao and Shen (2003) , Grassberger (1988) ,Holste et al. (1998) , Bonachela et al. (2008) , Marcon et al. (2014) which is actually the max value of "ChaoShen" and "Grassberger", Zhang and Grabchak (2014) , Chao et al. (2015) , Chao and Jost (2015) and Marcon (2015) .
The ChaoJost estimator Chao et al. (2013); Chao and Jost (2015)
contains
an unbiased part concerning observed species, equal to that of
Zhang and Grabchak (2014)
, and a (biased) estimator of the remaining
bias based on the estimation of the species-accumulation curve.
It is very efficient but slow if the number of individuals is more than a few hundreds.
The unveiled estimators rely on Chao et al. (2015)
,
completed by Marcon (2015)
.
The actual probabilities of observed species are estimated and completed by
a geometric distribution of the probabilities of unobserved species.
The number of unobserved species is estimated by the Chao1 estimator (UnveilC),
following Chao et al. (2015)
, or by the iChao1 (UnveiliC)
or the jackknife (UnveilJ).
The UnveilJ correction often has a lower bias but a greater variance
(Marcon 2015)
.
It is a good first choice thanks to the versatility of the jackknife
estimator of richness.
Estimators by Bonachela et al. (2008) and Holste et al. (1998) are rarely used.
To estimate \(\gamma\) diversity, the size of a metacommunity (see
metacommunity) is unknown so it has to be set according to a rule which does
not ensure that its abundances are integer values.
Then, classical bias-correction methods do not apply.
Providing the sample_coverage argument allows applying the ChaoShen and
Grassberger corrections to estimate quite well the entropy.
Diversity can be estimated at a specified level of interpolation or extrapolation, either a chosen sample size or sample coverage (Chao et al. 2014) , rather than its asymptotic value. See accum_hill for details.
Bonachela JA, Hinrichsen H, Muñoz MA (2008).
“Entropy Estimates of Small Data Sets.”
Journal of Physics A: Mathematical and Theoretical, 41(202001), 1--9.
doi:10.1088/1751-8113/41/20/202001
.
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
.
Chao A, Hsieh TC, Chazdon RL, Colwell RK, Gotelli NJ (2015).
“Unveiling the Species-Rank Abundance Distribution by Generalizing Good-Turing Sample Coverage Theory.”
Ecology, 96(5), 1189--1201.
doi:10.1890/14-0550.1
.
Chao A, Jost L (2015).
“Estimating Diversity and Entropy Profiles via Discovery Rates of New Species.”
Methods in Ecology and Evolution, 6(8), 873--882.
doi:10.1111/2041-210X.12349
.
Chao A, Shen T (2003).
“Nonparametric Estimation of Shannon's Index of Diversity When There Are Unseen Species in Sample.”
Environmental and Ecological Statistics, 10(4), 429--443.
doi:10.1023/A:1026096204727
.
Chao A, Wang Y, Jost L (2013).
“Entropy and the Species Accumulation Curve: A Novel Entropy Estimator via Discovery Rates of New Species.”
Methods in Ecology and Evolution, 4(11), 1091--1100.
doi:10.1111/2041-210x.12108
.
Grassberger P (1988).
“Finite Sample Corrections to Entropy and Dimension Estimates.”
Physics Letters A, 128(6-7), 369--373.
doi:10.1016/0375-9601(88)90193-4
.
Hill MO (1973).
“Diversity and Evenness: A Unifying Notation and Its Consequences.”
Ecology, 54(2), 427--432.
doi:10.2307/1934352
.
Holste D, Große I, Herzel H (1998).
“Bayes' Estimators of Generalized Entropies.”
Journal of Physics A: Mathematical and General, 31(11), 2551--2566.
Marcon E (2015).
“Practical Estimation of Diversity from Abundance Data.”
HAL, 01212435(version 2).
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
.
Zhang Z, Grabchak M (2014).
“Nonparametric Estimation of Kullback-Leibler Divergence.”
Neural computation, 26(11), 2570--2593.
doi:10.1162/NECO_a_00646
, 25058703.
# Diversity of each community
div_hill(paracou_6_abd, q = 2)
#> # A tibble: 4 × 5
#> site weight estimator order diversity
#> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 subplot_1 1.56 UnveilJ 2 42.3
#> 2 subplot_2 1.56 UnveilJ 2 44.6
#> 3 subplot_3 1.56 UnveilJ 2 48.8
#> 4 subplot_4 1.56 UnveilJ 2 36.0
# gamma diversity
div_hill(paracou_6_abd, q = 2, gamma = TRUE)
#> # A tibble: 1 × 4
#> site estimator order diversity
#> <chr> <chr> <dbl> <dbl>
#> 1 Metacommunity UnveilJ 2 46.5
# At 80% coverage
div_hill(paracou_6_abd, q = 2, level = 0.8)
#> # A tibble: 4 × 6
#> site weight estimator order level diversity
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 subplot_1 1.56 Chao2014 2 304 37.2
#> 2 subplot_2 1.56 Chao2014 2 347 39.6
#> 3 subplot_3 1.56 Chao2014 2 333 42.7
#> 4 subplot_4 1.56 Chao2014 2 303 32.3