Model-based thinking for community ecology

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.