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rarestr

An R Package of Rarefaction-Based Species Richness Estimator

https://github.com/pzhaonet/rarestr

Science Score: 39.0%

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Keywords

biodiversity ecology r
Last synced: 10 months ago · JSON representation

Repository

An R Package of Rarefaction-Based Species Richness Estimator

Basic Info
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  • Stars: 2
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Topics
biodiversity ecology r
Created almost 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

R-CMD-check

Introduction

rarestR is an R package of rarefaction-based species richness estimator. This package is designed to calculate rarefaction-based α- and β-diversity. It also offers parametric extrapolation to estimate the total expected species in a single community and the total expected shared species between two communities. The package also provides visualization of the curve-fitting for these estimators.

Installation

# Stable version
install.packages('rarestR')
# Development version
remotes::install_github('pzhaonet/rarestR')

Load rarestR and the demo dataset

library(rarestR)
data("share")

The dataset share is a matrix with 3 rows and 142 columns. It comprises three samples randomly drawn from three simulated communities. Every community consists of 100 species with approximately 100,000 individuals following a log-normal distribution (mean = 6.5, SD = 1). Setting the first community as control group, the second and third community shared a total of 25 and 50 species with the control. A more detailed description of the control and scenario groups can be found in Zou and Axmacher (2021). The share dataset represents a random subsample of 100, 150 and 200 individuals from three three communities, containing 58, 57 and 74 species, respectively.

Calculate the Expected Species

es(share, m = 100)

##        1        2        3 
## 58.00000 47.77653 53.00568

es(share, method = "b", m = 100)

##        1        2        3 
## 43.51041 40.74378 46.19118

# When the m is larger than the total sample size, "NA" will be filled:
es(share, m = 150)

## Warning in es(y, m, method): m can not be larger than the total sample size

##        1        2        3 
##       NA 57.00000 65.24147

Compute dissimilarity estimates between two samples based on Expected Species Shared (ESS)-measures

ess(share)

##           1         2
## 2 0.7970962          
## 3 0.6359703 0.7642330

ess(share, m = 100)

##           1         2
## 2 0.8566624          
## 3 0.7308390 0.8229221

ess(share, m = 100, index = "ESS")

##          1        2
## 2 13.01735         
## 3 22.65674 13.23924

Calculate and visualize the Total Expected Species base on ESa, ESb and their average value

Output_tes <- tes(share[1,])
Output_tes

##          est est.sd model.par
## TESa  138.50   2.46  logistic
## TESb   92.63  32.65   Weibull
## TESab 115.56  16.37      <NA>

plot(Output_tes)

Calculate and visualize the Total number of Expected Shared Species between two samples

Output_tess <- tess(share[1:2,])
Output_tess

##     est est.sd model.par
## 1 23.28   2.59  logistic

plot(Output_tess)

Reference

Zou Y, Zhao P, Wu N, Lai J, Peres-Neto PR, Axmacher JC (2025). “rarestR: An R Package Using Rarefaction Metrics to Estimate α- and β-Diversity for Incomplete Samples.” Diversity and Distributions, 31(1), e13954. doi:10.1111/ddi.13954.

Owner

  • Name: Peng Zhao
  • Login: pzhaonet
  • Kind: user
  • Location: Suzhou, China
  • Company: Xi'an Jiaotong-Liverpool University

Assistant Professor in Environmental Science

GitHub Events

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Last synced: over 2 years ago

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Past Year
  • Commits: 20
  • Committers: 2
  • Avg Commits per committer: 10.0
  • Development Distribution Score (DDS): 0.15
Top Committers
Name Email Commits
Peng Zhao p****o@p****t 17
YiZou01 6****1 3
Committer Domains (Top 20 + Academic)
pzhao.net: 1

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Packages

  • Total packages: 1
  • Total downloads:
    • cran 219 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 5
  • Total maintainers: 1
cran.r-project.org: rarestR

Rarefaction-Based Species Richness Estimator

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 219 Last month
Rankings
Dependent packages count: 27.9%
Dependent repos count: 36.9%
Average: 50.3%
Downloads: 86.1%
Maintainers (1)
pengzhao20@outlook.com
Last synced: 10 months ago