Fit and Compare Species-Area Relationship Models Using Multimodel Inference • sars

fit and compare Species-Area Relationship (SAR) models using multi-model inference

sars provides functionality to fit twenty SAR model using non-linear regression, and to calculate multi-model averaged curves using various information criteria. The software also provides easy to use functionality to plot multi-model SAR curves and to generate confidence intervals using bootstrapping. Additional SAR related functions include fitting the linear version of the power model and comparing parameters with the non-linear version, fitting the general dynamic model of island biogeography, fitting the random placement model to a species abundance - site matrix, and extrapolating fitted SAR models to predict richness on larger islands / sample areas.

As this is version 1.2.2 of the package, it is possible that there are some bugs in places. Please report any issues to us via GitHub.

The package has an associated vignette that provides examples of how to use the package.

A website for the package can be found here: https://txm676.github.io/sars/

Version 1.1.1 of the package has been archived on the Zenodo research data repository (DOI: 10.5281/zenodo.2573067).

Installation
Example
References

Installation

You can install the released version of sars from CRAN with:

install.packages("sars")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("txm676/sars")

Example usage

Basic usage of sars will result in using two types of functions:

To fit the power sar model (Arrhenius 1921) to the ‘galapagos’ (Preston 1962) data set:

fit_pow <- sar_power(data = galap)
fit_pow
#> 
#> Model:
#> Power
#> 
#> Call:
#> S == c * A^z
#> 
#> Coefficients:
#>          c          z 
#> 33.1791553  0.2831868

Attempting to fit all 20 sar models to the ‘galapagos’ (Preston 1962) data set and get a multi-model SAR:

mm_galap <- sar_average(data = galap)
#> 
#>  Now attempting to fit the 20 SAR models: 
#> 
#> --  multi_sars --------------------------- multi-model SAR --
#> > power    : v
#> > powerR   : v
#> > epm1     : v
#> > epm2     : v
#> > p1       : v
#> > p2       : v
#> > loga     : v
#> > koba     : v
#> > mmf      : v
#> > monod    : v
#> > negexpo  : v
#> > chapman  : Warning: could not compute parameters statistics
#> > weibull3 : v
#> > asymp    : v
#> > ratio    : v
#> > gompertz : v
#> > weibull4 : v
#> > betap    : v
#> > heleg    : v
#> > linear   : v
#> 
#> Model fitting completed - all models succesfully fitted.  Now undertaking model validation checks.
#> Additional models  will be excluded if necessary:
#> 
#> 4 models failed the residuals normality test and  have been excluded  from the multi SAR:
#> Extended Power model 1, Asymptotic regression, Cumulative Weibull 4 par., Linear model
#> 16 remaining models used to construct the multi  SAR:
#>  Power, PowerR, Extended Power model 2, Persistence function 1, Persistence function 2, Logarithmic, Kobayashi, MMF, Monod, Negative exponential, Chapman Richards, Cumulative Weibull 3 par., Rational function, Gompertz, Beta-P cumulative, Heleg(Logistic) 
#> -------------------------------------------------------------

Each of the ‘fitted’ objects have corresponding plot methods:

To fit the logarithmic SAR model (Gleason 1922) to the ‘galapagos’ data set and plot it

fit_loga <- sar_loga(data = galap)

plot(fit_loga)

To fit a multimodel SAR curve to the ‘galapagos’ data set and plot it (alongside the individual model fits)

mm_galap <- suppressMessages(sar_average(data = galap, verb = FALSE))
mm_galap 
#> 
#> This is a sar_average fit object:
#> 
#> 16 models successfully fitted
#> 
#> 4 models were unable to be fitted or were removed due to model checks
#> 
#> AICc used to rank models


plot(mm_galap, pLeg = FALSE, mmSep = TRUE)