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, and fitting the random placement model to a species abundance - site matrix.

As this is version 1.1.0 of the package, it is possible that there are some bugs in places. Please report any issues to us via GitHub.

• Installation
• Example
• Troubleshoutting
• References

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")

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)

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

mm_galap <- suppressMessages(sar_average(data = galap, verb = FALSE))

plot(mm_galap, pLeg = FALSE)

If, despite the ❤️ brought during the programming of this R 📦 and writing of this documentation, you have difficulties to install or run sars, if you have questions about the procedures or calculations, or if you want to report bugs 🪲, do not hesitate to connect with us on GitHub.

Arrhenius, Olof. 1921. “Species and Area.” The Journal of Ecology 9 (1). British Ecological Society:95. https://doi.org/10.2307/2255763.

Gleason, Henry Allan. 1922. “On the Relation Between Species and Area.” Ecology 3 (2). Ecological Society of America:158–62. https://doi.org/10.2307/1929150.

Preston, F. W. 1962. “The Canonical Distribution of Commonness and Rarity: Part I.” Ecology 43 (2). Ecological Society of America:185. https://doi.org/10.2307/1931976.