rarestR: An R package using rarefaction metrics to estimate α-diversity and β-diversity for incomplete samples

Introduction

rarestR is an R package of rarefaction-based species richness estimator. This package is designed to calculate rarefaction-based $\alpha$- and $\beta$-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(x = 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)

Customize the plots

The rarestR package provides a S3 method to use the generic plot() function for visualizing the output of the tes() and tess() functions in a fast way and consistent way, which is friendly to R beginners. Advanced R users might want to customize the plots. They can extract the modelled data and use other data visualization functions. Here we demonstrate how to customize the plot of the Total Expected Species calculated on the basis of the previous example.

The tes() function returns a list (Output_tes) with (1) a data frame ($tbl) of the summary of the estimated values and their standard deviations based on TESa, TESb, and TESab, and the model used in the estimation of TES, either ‘logistic’ or ‘Weibull’., (2) a list ($TESa) of the modeled results with the TESa method, and (3) a list ($TESb) of the modelled results with the TESb method. By default, only the data frame is printed in the R console when printing Output_tes:

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>

Output_tes$tbl

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

You can explicitly print the two lists as well:

Output_tes$TESa
Output_tes$TESb

or see their structures:

str(Output_tes$TESa)

## List of 5
##  $ par   : Named chr [1:3] "138.5" "2.46" "logistic"
##   ..- attr(*, "names")= chr [1:3] "est" "est.sd" "model.par"
##  $ result:'data.frame':  33 obs. of  2 variables:
##   ..$ value: num [1:33] 1.98 2.95 3.91 4.85 5.78 ...
##   ..$ Logm : num [1:33] 0.693 1.099 1.386 1.609 1.792 ...
##  $ xmax  : num 8.88
##  $ Predx : num [1:1000] 0 0.0178 0.0355 0.0533 0.0711 ...
##  $ Predy : num [1:1000] 0.996 1.013 1.031 1.05 1.068 ...

str(Output_tes$TESb)

## List of 5
##  $ par   : Named chr [1:3] "92.63" "32.65" "Weibull"
##   ..- attr(*, "names")= chr [1:3] "est" "est.sd" "model.par"
##  $ result:'data.frame':  33 obs. of  2 variables:
##   ..$ value: num [1:33] 1.97 2.92 3.85 4.76 5.64 ...
##   ..$ Logm : num [1:33] 0.693 1.099 1.386 1.609 1.792 ...
##  $ xmax  : num 7.1
##  $ Predx : num [1:1000] 0 0.0142 0.0284 0.0427 0.0569 ...
##  $ Predy : num [1:1000] 2.27 2.27 2.27 2.27 2.27 ...

Thus, you can extract the simulated results and predicted x and y values for TESa:

Output_tes$TESa$result
Output_tes$TESa$Predx
Output_tes$TESa$Predy

or TESb in a similar way.

Then, you can visualize these data in your favourite way. In the following example, we use the base R functions with rotated y labels (las = 1), logarithmic y axis (log = "y"), and blue points (‘col = “blue”’):

plot(Output_tes$TESa$Predx, Output_tes$TESa$Predy, type = 'l', las = 1, 
     xlab = 'ln(m)', ylab = 'ES', log = 'y')
points(Output_tes$TESa$result$Logm, Output_tes$TESa$result$value, col = 'blue')

You can use ggplot2 as well. Here is an example (not shown):

library(ggplot2)
ggplot() +
  geom_line(aes(Output_tes$TESa$Predx, Output_tes$TESa$Predy)) +
  geom_point(aes(Logm, value), data = Output_tes$TESa$result, colour = 'red', shape = 1, size = 3) +
  geom_hline(yintercept = as.numeric(Output_tes$TESa$par[1]), linetype = 2) + 
  lims(x = c(0, 20), y = c(0, 150)) +
  labs(x = 'ln(m)', y = 'ES')

For more details, see the Details section in the help documents for tes() and tess().

References

Zou, Y, & Axmacher, JC (2020). The Chord-Normalized Expected Species Shared (CNESS)- distance represents a superior measure of species turnover patterns. , 11(2), 273-280. doi:10.1111/2041-210X.13333

Zou, Y, & Axmacher, JC (2021). Estimating the number of species shared by incompletely sampled communities. Ecography, 44(7), 1098-1108. doi:10.1111/ecog.05625

Zou Y, Zhao P, Axmacher JC (2023). Estimating total species richness: Fitting rarefaction by asymptotic approximation. Ecosphere, 14(1), e4363. doi:10.1002/ecs2.4363.