SARS: an R package for fitting, evaluating and comparing species–area relationship models

Research output: Contribution to journalArticle

Authors

  • Tom Matthews
  • Kostas Triantis
  • Robert J. Whittaker
  • Francois Guilhaumon

Colleges, School and Institutes

External organisations

  • Department of Ecology and Taxonomy, Faculty of Biology, National and Kapodistrian, University of Athens, Athens GR-15784, Greece
  • Conservation Biogeography and Macroecology Programme, School of Geography and the Environment, University of Oxford
  • Center for Macroecology, Evolution and Climate, Department of Biology, University of Copenhagen
  • IRD UMR 9190 MARBEC, IRD-CNRS-IFREMER-UM, Université de Montpellier

Abstract

The species–area relationship (SAR) constitutes one of the most general ecological patterns globally. A number of different SAR models have been proposed. Recent work has shown that no single model universally provides the best fit to empirical SAR datasets: multiple models may be of practical and theoretical interest. However, there are no software packages available that a) allow users to fit the full range of published SAR models, or b) provide functions to undertake a range of additional SAR‐related analyses. To address these needs, we have developed the R package ‘sars’ that provides a wide variety of SAR‐related functionality. The package provides functions to: a) fit 20 SAR models using non‐linear and linear regression, b) calculate multi‐model averaged curves using various information criteria, and c) generate confidence intervals using bootstrapping. Plotting functions allow users to depict and scrutinize the fits of individual models and multi‐model averaged curves. The package also provides additional SAR functionality, including functions to fit, plot and evaluate the random placement model using a species–sites abundance matrix, and to fit the general dynamic model of oceanic island biogeography. The ‘sars’ R package will aid future SAR research by providing a comprehensive set of simple to use tools that enable in‐depth exploration of SARs and SAR‐related patterns. The package has been designed to allow other researchers to add new functions and models in the future and thus the package represents a resource for future SAR work that can be built on and expanded by workers in the field.

Details

Original languageEnglish
Pages (from-to)1353-1457
Number of pages10
JournalEcography
Volume42
Issue number8
Early online date15 Mar 2019
Publication statusPublished - Aug 2019

Keywords

  • Diversity–area relationship, general dynamic mode, island biogeography, multi-model inference, power model, random placement model, sars, species accumulation curve, species–area relationship, diversity–area relationship