Plant Ecology

, Volume 216, Issue 5, pp 669–682 | Cite as

Model-based thinking for community ecology

  • David I. Warton
  • Scott D. Foster
  • Glenn De’ath
  • Jakub Stoklosa
  • Piers K. Dunstan
Article

Abstract

In this paper, a case is made for the use of model-based approaches for the analysis of community data. This involves the direct specification of a statistical model for the observed multivariate data. Recent advances in statistical modelling mean that it is now possible to build models that are appropriate for the data which address key ecological questions in a statistically coherent manner. Key advantages of this approach include interpretability, flexibility, and efficiency, which we explain in detail and illustrate by example. The steps in a model-based approach to analysis are outlined, with an emphasis on key features arising in a multivariate context. A key distinction in the model-based approach is the emphasis on diagnostic checking to ensure that the model provides reasonable agreement with the observed data. Two examples are presented that illustrate how the model-based approach can provide insights into ecological problems not previously available. In the first example, we test for a treatment effect in a study where different sites had different sampling intensities, which was handled by adding an offset term to the model. In the second example, we incorporate trait information into a model for ordinal response in order to identify the main reasons why species differ in their environmental response.

Keywords

Community-level modelling Fourth-corner problem Model checking Multivariate analysis Ordination Species distribution models 

Notes

Acknowledgments

DIW is supported by the Australian Research Council Future Fellow scheme (project number FT120100501). SDF, GD and PKD were supported by the Marine Biodiversity Hub, a collaborative partnership supported through funding from the Australian Government’s National Environmental Research Program (NERP). NERP Marine Biodiversity Hub partners include the Institute for Marine and Antarctic Studies, University of Tasmania; CSIRO Wealth from Oceans National Flagship, Geoscience Australia, Australian Institute of Marine Science, Museum Victoria, Charles Darwin University and the University of Western Australia.

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • David I. Warton
    • 1
  • Scott D. Foster
    • 2
    • 3
  • Glenn De’ath
    • 4
  • Jakub Stoklosa
    • 1
  • Piers K. Dunstan
    • 2
  1. 1.School of Mathematics and Statistics and Evolution & Ecology Research CentreThe University of New South WalesSydneyAustralia
  2. 2.CSIRO’s Wealth from Oceans FlagshipHobartAustralia
  3. 3.CSIRO’s Division of Computational InformaticsHobartAustralia
  4. 4.Australian Institute of Marine ScienceCape FergusonAustralia

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