| Pred.abundance.rare {RSE} | R Documentation |
Abundance-based data: predicting the number of new rare species
Description
Abundance-based prediction on the number of new rare species using a Bayesian-weight and two unweighted estimators along with their bootstrap standard errors and 95% bootstrap confidence intervals.
Usage
Pred.abundance.rare(boot.rep = 100, f = NULL, xi = NULL, m, k.show = 3)
Arguments
boot.rep |
Replicate number of the bootstrapping procedure |
f |
A vector of species frequency counts, i.e., the number of singleton species (only one individual observed in the sample), the number of doubleton species (two individuals observed in the sample), and so forth. |
xi |
A vector of species abundance data, i.e., the number of individuals of species 1, the number of individuals of species 2, and so forth. |
m |
The number of individuals of an additional sample |
k.show |
Display the estimating result of the numbers of extremely rare species with abundance <= k.show in the additional sample |
Value
Estimating results including point estimate, bootstrap standard error, and 95 % bootstrap confidence interval for each of three methods (a Bayesian-weight and two unweighted estimators)
Author(s)
Youhua Chen & Tsung-Jen Shen
References
Shen TJ, Chen YH (2018) A Bayesian weighted approach to predicting the number of newly discovered rare species. Conservation Biology, In press.
See Also
Examples
## As an example, Herpetological assemblage data are used here.
data(HerpetologicalData)
## two columns represent two samples of species abundance data
X.merge = HerpetologicalData
## the first column is treated as the original sample
X.col1 = X.merge[,1]
## the second column is treated as the additional sample
X.col2 = X.merge[,2]
Xi = X.col1
## Convert species abundance data to species frequency counts data
f = X.to.f(Xi)
## the number of individuals of the additional sample
m = sum(X.col2)
Pred.abundance.rare(f=f, m=m)