Learn R Programming

RSE (version 1.3)

Number of Newly Discovered Rare Species Estimation

Description

A Bayesian-weighted estimator and two unweighted estimators are developed to estimate the number of newly found rare species in additional ecological samples. Among these methods, the Bayesian-weighted estimator and an unweighted (Chao-derived) estimator are of high accuracy and recommended for practical applications. Technical details of the proposed estimators have been well described in the following paper: Shen TJ, Chen YH (2018) A Bayesian weighted approach to predicting the number of newly discovered rare species. Conservation Biology, In press.

Copy Link

Version

Install

install.packages('RSE')

Monthly Downloads

155

Version

1.3

License

GPL-3

Maintainer

Youhua Chen

Last Published

November 20th, 2018

Functions in RSE (1.3)

X.to.f

Data transformation: from species abundance data to species frequency counts data
Pred.abundance.rare

Abundance-based data: predicting the number of new rare species
DetAbu

Abundance-based data: the estimation of parameters for obtaining the estimation of relative abundances of observed species
Pred.incidence.rare

Incidence-based data: predicting the number of new rare species
RSE-package

RSE
SpEst.Chao1.abun

Species richness estimation
boot.abundance.fun

Generate a bootstrap abundance-based sample
f.to.X

Data transformation: from species frequency counts to species abundance data
boot.incidence.fun

Generate a bootstrap incidence-based sample
CanadaMite

mite incidence in moss patches of 32 locations of western Canada (Chen et al. 2015)
DetInc

Incidence-based data: the estimation of parameters for obtaining the estimation of detection probabilites of observed species
HerpetologicalData

Abundance of herpetofauna in the conserved and human disturbed areas of Mexico (Suazo-Ortuno et al. 2008)
Pred.Fk.BW

Abundance-based data: Bayesian-weight estimator
Pred.Fk.Naive

Abundance-based data: unweighted naive estimator
Pred.Fk.unweighted

Abundance-based data: Unweighted estimator
Pred.Qk.BW

Incidence-based data: Bayesian-weight estimator
Pred.Qk.Naive

Incidence-based data: unweighted naive estimator
Pred.Qk.unweighted

Incidence-based data: Unweighted Estimator