Calculates the number of species from probability vector. The name is that of the estimator (the bias correction) used.
Richness(NorP, ...) bcRichness(Ns, Correction = "Best", Alpha = 0.05, JackOver = FALSE, JackMax = 10, CheckArguments = TRUE) # S3 method for ProbaVector Richness(NorP, ..., CheckArguments = TRUE, Ps = NULL) # S3 method for AbdVector Richness(NorP, Correction = "Best", Alpha = 0.05, JackOver = FALSE, JackMax = 10, Level = NULL, PCorrection = "Chao2015", Unveiling = "geom", RCorrection = "Rarefy", ..., CheckArguments = TRUE, Ns = NULL) # S3 method for integer Richness(NorP, Correction = "Best", Alpha = 0.05, JackOver = FALSE, JackMax = 10, Level = NULL, PCorrection = "Chao2015", Unveiling = "geom", RCorrection = "Rarefy", ..., CheckArguments = TRUE, Ns = NULL) # S3 method for numeric Richness(NorP, Correction = "Best", Alpha = 0.05, JackOver = FALSE, JackMax = 10, Level = NULL, PCorrection = "Chao2015", Unveiling = "geom", RCorrection = "Rarefy", ..., CheckArguments = TRUE, Ps = NULL, Ns = NULL)
A probability vector, summing to 1.
A numeric vector containing species abundances.
A string containing one of the possible corrections:
The risk level, 5% by default, used to optimize the jackknife order.
The highest jackknife order allowed. Default is 10. Allowed values are between 1 and 9.
The level of interpolation or extrapolation. It may be an a chosen sample size (an integer) or a sample coverage (a number between 0 and 1). Richness extrapolation require its asymptotic estimation depending on the choice of
A string containing one of the possible corrections to estimate a probability distribution in
A string containing one of the possible unveiling methods to estimate the probabilities of the unobserved species in
A string containing a correction recognized by
Additional arguments. Unused.
Bias correction requires the number of individuals. Use
bcRichness and choose the
Chao correction techniques are from Chao (1984) and Chiu et al. (2015). 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; 1979). The argument
JackOver allows selecting one order over the optimal one.
Many other estimators are available elsewhere, the ones implemented here are necessary for other entropy estimations.
The functions are designed to be used as simply as possible.
Richness is a generic method. If its first argument is an abundance vector, an integer vector or a numeric vector which does not sum to 1, the bias corrected function
bcRichness is called.
Richness 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.
Extrapolation relies on the estimation of the asymptotic richness. If
PCorrection is "None", then the asymptotic estimation of richness is made using the chosen
Correction, else the asymtpotic distribution of the community is derived and its estimated richness adjusted so that the entropy of a sample of this distribution of the size of the actual sample has the entropy of the actual sample.
A named number equal to the estimated number of species.
The name is the
Correction, except for "SAC" (Species Accumulation Curve) for interpolation.
Burnham, K. P., and Overton,W. S. (1978), Estimation of the Size of a Closed Population When Capture Probabilities Vary Among Animals. Biometrika, 65: 625-633.
Burnham, K. P., and Overton,W. S. (1979), Robust Estimation of Population Size When Capture Probabilities Vary Among Animals. Ecology 60:927-936.
Chao, A. (1984) Nonparametric estimation of the number of classes in a population. Scandinavian Journal of Statistics 11: 265-270.
Chao, A., Gotelli, N. J., Hsieh, T. C., Sander, E. L., Ma, K. H., Colwell, R. K., Ellison, A. M (2014). Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecological Monographs, 84(1): 45-67.
Chiu, C.-H., Wang, Y.-T., Walther, B. A., Chao, A. (2014) An Improved Nonparametric Lower Bound of Species Richness via a Modified Good-Turing Frequency Formula. Biometrics 70(3): 671-682.
Eric Marcon <Eric.Marcon@ecofog.gf>
# Load Paracou data (number of trees per species in two 1-ha plot of a tropical forest) data(Paracou618) # Ns is the total number of trees per species Ns <- as.AbdVector(Paracou618.MC$Ns) # Species probabilities Ps <- as.ProbaVector(Paracou618.MC$Ns) # Whittaker plot plot(Ns)# Number of observed species Richness(Ps)#> None #> 229# Estimate the actual number of species bcRichness(Ns, Correction = "Chao1")#> Chao1 #> 314.9348bcRichness(Ns, Correction = "iChao1")#> iChao1 #> 333.7466bcRichness(Ns, Correction = "Jackknife")#> Jackknife 2 #> 359bcRichness(Ns, Correction = "Jackknife", JackOver=TRUE)#> Jackknife 3 #> 381# Jackknife estimation is package SPECIES provides confidence interval SPECIES::jackknife(AbdFreqCount(Ns))#> #> Your specified order is larger than that determined by the test, #> Therefore the order from the test is used. #>#> $JackknifeOrder #>  2 #> #> $Nhat #>  359 #> #> $SE #>  22.84732 #> #> $CI #> lb ub #> [1,] 314 404 #>